quantitative research about mental health of students

This paper is in the following e-collection/theme issue:

Published on 3.9.2020 in Vol 22 , No 9 (2020) : September

Effects of COVID-19 on College Students’ Mental Health in the United States: Interview Survey Study

Authors of this article:

Original Paper

• Changwon Son 1 , BS, MS   ; 
• Sudeep Hegde 1 , BEng, MS, PhD   ; 
• Alec Smith 1 , BS   ; 
• Xiaomei Wang 1 , BS, PhD   ; 
• Farzan Sasangohar 1, 2 , BA, BCS, MASc, SM, PhD  

1 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States

2 Center for Outcomes Research, Houston Methodist Hospital, Houston, TX, United States

Corresponding Author:

Farzan Sasangohar, BA, BCS, MASc, SM, PhD

Department of Industrial and Systems Engineering

Texas A&M University

College Station, TX, 77843

United States

Phone: 1 979 458 2337

Email: [email protected]

Background: Student mental health in higher education has been an increasing concern. The COVID-19 pandemic situation has brought this vulnerable population into renewed focus.

Objective: Our study aims to conduct a timely assessment of the effects of the COVID-19 pandemic on the mental health of college students.

Methods: We conducted interview surveys with 195 students at a large public university in the United States to understand the effects of the pandemic on their mental health and well-being. The data were analyzed through quantitative and qualitative methods.

Results: Of the 195 students, 138 (71%) indicated increased stress and anxiety due to the COVID-19 outbreak. Multiple stressors were identified that contributed to the increased levels of stress, anxiety, and depressive thoughts among students. These included fear and worry about their own health and of their loved ones (177/195, 91% reported negative impacts of the pandemic), difficulty in concentrating (173/195, 89%), disruptions to sleeping patterns (168/195, 86%), decreased social interactions due to physical distancing (167/195, 86%), and increased concerns on academic performance (159/195, 82%). To cope with stress and anxiety, participants have sought support from others and helped themselves by adopting either negative or positive coping mechanisms.

Conclusions: Due to the long-lasting pandemic situation and onerous measures such as lockdown and stay-at-home orders, the COVID-19 pandemic brings negative impacts on higher education. The findings of our study highlight the urgent need to develop interventions and preventive strategies to address the mental health of college students.

Introduction

Mental health issues are the leading impediment to academic success. Mental illness can affect students’ motivation, concentration, and social interactions—crucial factors for students to succeed in higher education [ 1 ]. The 2019 Annual Report of the Center for Collegiate Mental Health [ 2 ] reported that anxiety continues to be the most common problem (62.7% of 82,685 respondents) among students who completed the Counseling Center Assessment of Psychological Symptoms, with clinicians also reporting that anxiety continues to be the most common diagnosis of the students that seek services at university counseling centers. Consistent with the national trend, Texas A&M University has seen a rise in the number of students seeking services for anxiety disorders over the past 8 years. In 2018, slightly over 50% of students reported anxiety as the main reason for seeking services. Despite the increasing need for mental health care services at postsecondary institutions, alarmingly, only a small portion of students committing suicide contact their institution counseling centers [ 3 ], perhaps due to the stigma associated with mental health. Such negative stigma surrounding mental health diagnosis and care has been found to correlate with a reduction in adherence to treatment and even early termination of treatment [ 4 ].

The COVID-19 pandemic has brought into focus the mental health of various affected populations. It is known that the prevalence of epidemics accentuates or creates new stressors including fear and worry for oneself or loved ones, constraints on physical movement and social activities due to quarantine, and sudden and radical lifestyle changes. A recent review of virus outbreaks and pandemics documented stressors such as infection fears, frustration, boredom, inadequate supplies, inadequate information, financial loss, and stigma [ 5 ]. Much of the current literature on psychological impacts of COVID-19 has emerged from the earliest hot spots in China. Although several studies have assessed mental health issues during epidemics, most have focused on health workers, patients, children, and the general population [ 6 , 7 ]. For example, a recent poll by The Kaiser Family Foundation showed that 47% of those sheltering in place reported negative mental health effects resulting from worry or stress related to COVID-19 [ 8 ]. Nelson et al [ 9 ] have found elevated levels of anxiety and depressive symptoms among general population samples in North America and Europe. However, with the exception of a few studies, notably from China [ 10 - 12 ], there is sparse evidence of the psychological or mental health effects of the current pandemic on college students, who are known to be a vulnerable population [ 13 ]. Although the findings from these studies thus far converge on the uptick of mental health issues among college students, the contributing factors may not necessarily be generalizable to populations in other countries. As highlighted in multiple recent correspondences, there is an urgent need to assess effects of the current pandemic on the mental health and well-being of college students [ 14 - 17 ].

The aim of this study is to identify major stressors associated with the COVID-19 pandemic and to understand their effects on college students’ mental health. This paper documents the findings from online interview surveys conducted in a large university system in Texas.

Study Design

A semistructured interview survey guide was designed with the purpose of assessing the mental health status of college students both quantitatively and qualitatively. In addition, the interview aimed to capture the ways that students have been coping with the stress associated with the pandemic situation. First, our study assesses participants’ general stress levels using the Perceived Stress Scale-10 (PSS) [ 18 ]. PSS is a widely used instrument to measure overall stress in the past month [ 19 ]. Second, participants were asked if their own and peers’ (two separate questions) stress and anxiety increased, decreased, or remained the same because of the COVID-19 pandemic. For those who indicated increased stress and anxiety during the pandemic, we questioned their stress coping strategies and use of available mental health counseling services. We then elicited pandemic-specific stressors and their manifestations across 12 academic-, health-, and lifestyle-related categories of outcomes such as effects on own or loved ones’ health, sleeping habits, eating habits, financial situation, changes to their living environment, academic workload, and social relations. Students were also asked about the impact of COVID-19 on depressive and suicidal thoughts. These constructs were derived from existing literature identifying prominent factors affecting college students’ mental health [ 20 , 21 ]. Feedback on the severity of COVID-19’s impact on these aspects were elicited using a 4-point scale: 0 (none), 1 (mild), 2 (moderate), and 3 (severe). Participants were asked to elaborate on each response. Third, participants were guided to describe stressors, coping strategies, and barriers to mental health treatment during a typical semester without associating with the COVID-19 pandemic. Although multiple analyses of the collected data are currently under progress, PSS results and the COVID-19–related findings are presented in this paper.

Participants

Participants were recruited from the student population of a large university system in Texas, United States. This particular university closed all their campuses on March 23, 2020, and held all its classes virtually in response to the COVID-19 pandemic. In addition, the state of Texas issued a stay-at-home order on April 2, 2020. Most interviews were conducted about 1 month after the stay-at-home order in April 2020. Figure 1 illustrates the trend of cumulative confirmed cases and a timeline of major events that took place in the university and the state of Texas. Participants were recruited by undergraduate student researchers through email, text messaging, and snowball sampling. The only inclusion criteria for participation was that participants should have been enrolled as undergraduate students in the university at the time of the interviews.

The interviews were conducted by 20 undergraduate researchers trained in qualitative methods and the use of the interview survey guide described above. None of the authors conducted the interviews. All interviews were conducted via Zoom [ 22 ] and were audio recorded. The recordings were later transcribed using Otter.ai [ 23 ], an artificial intelligence–based transcription service, and verified for accuracy manually. Prior to the interview, participants were provided an information document about the study approved by the university’s Institutional Review Board (No 2019-1341D). Upon verbal consent, participants were asked to respond to a questionnaire about their demographic information such as age, gender, year of college, and program of study before completing the interview. Participation was voluntary and participants were not compensated.

Data Analysis

First, descriptive statistics were compiled to describe participants’ demographics (eg, age, gender, academic year, and major) and the distribution of the ratings on PSS-10 survey items. A total PSS score per participant was calculated by first reversing the scores of the positive items (4-7, 9, and 10) and then adding all the ten scores. A mean (SD) PSS score was computed to evaluate the overall level of stress and anxiety among the participants during the COVID-19 pandemic. Second, participants’ answers to 12 academic-, health-, and lifestyle-related questions were analyzed to understand relative impacts of the pandemic on various aspects of college students’ mental health. Percentages of participants who indicated negative ratings (ie, mild, moderate, or severe influence) on these questions were calculated and ranked in a descending order. Qualitative answers to the 12 stressors and coping strategies were analyzed using thematic analysis [ 24 , 25 ] similar to the deductive coding step in the grounded theory method [ 26 ]. A single coder (CS), trained in qualitative analysis methods, analyzed the transcripts and identified themes using an open coding process, which does not use a priori codes or codes created prior to the analysis and places an emphasis on information that can be extracted directly from the data. Following the identification of themes, the coder discussed the codes with two other coders (XW and AS) trained in qualitative analysis and mental health research to resolve discrepancies among related themes and discuss saturation. The coders consisted of two Ph.D. students and one postdoctoral fellow at the same university. MAXQDA (VERBI GmbH) [ 27 ] was used as a computer software program to carry out the qualitative analysis.

Of the 266 university students initially recruited by the undergraduate researchers, 17 retreated and 249 participated in this study. There were 3 graduate students and 51 participants who had missing data points and were excluded, and data from 195 participants were used in the analysis. The average age was 20.7 (SD 1.7) years, and there were more female students (111/195, 57%) than male students (84/195, 43%). Approximately 70% of the participants were junior and senior students. About 60% of the participants were majoring in the college of engineering, which was the largest college in the university population ( Table 1 ). The mean PSS score for the 195 participants was 18.8 (SD 4.9), indicating moderate perceived stress in the month prior to the interview ( Table 2 ).

a PSS: Perceived Stress Scale-10.

Challenges to College Students’ Mental Health During COVID-19

Out of 195 participants, 138 (71%) indicated that their stress and anxiety had increased due to the COVID-19 pandemic, whereas 39 (20%) indicated it remained the same and 18 (9%) mentioned that the stress and anxiety had actually decreased. Among those who perceived increased stress and anxiety, only 10 (5%) used mental health counseling services. A vast majority of the participants (n=189, 97%) presumed that other students were experiencing similar stress and anxiety because of COVID-19. As shown in Figure 2 , at least 54% (up to 91% for some categories) of participants indicated negative impacts (either mild, moderate, or severe) of COVID-19 on academic-, health-, and lifestyle-related outcomes. The qualitative analysis yielded two to five themes for each category of outcomes. The chronic health conditions category was excluded from the qualitative analysis due to insufficient qualitative response. Table 3 presents the description and frequency of the themes and select participant quotes.

a Not every participant provided sufficient elaboration to allow for identification of themes, so the frequency of individual themes does not add up to the total number of participants who indicated negative impacts of the COVID-19 outbreak.

b The five-digit alphanumeric value indicates the participant ID.

c TA: teaching assistant.

Concerns for One’s Own Health and the Health of Loved Ones

A vast majority of the participants (177/195, 91%) indicated that COVID-19 increased the level of fear and worry about their own health and the health of their loved ones. Over one-third of those who showed concern (76/177, 43%) were worried about their families and relatives who were more vulnerable, such as older adults, those with existing health problems, and those who are pregnant or gave birth to a child recently. Some of the participants (26/177, 15%) expressed their worry about their family members whose occupation increased their risk of exposure to COVID-19 such as essential and health care workers. Some participants (19/177, 11%) specifically mentioned that they were worried about contracting the virus.

Difficulty With Concentration

A vast majority of participants (173/195, 89%) indicated difficulty in concentrating on academic work due to various sources of distraction. Nearly half of them (79/173, 46%) mentioned that their home is a distractive environment and a more suitable place to relax rather than to study. Participants mentioned that they were more prone to be interrupted by their family members and household chores at home. Other factors affecting students’ concentration were lack of accountability (21/173, 12%) and social media, internet, and video games (19/173, 11%). Some (18/173, 10%) stated that online classes were subject to distraction due to lack of interactions and prolonged attention to a computer screen. Additionally, monotonous life patterns were mentioned by some to negatively affect concentration on academic work (5/173, 3%).

Disruption to Sleep Patterns

A majority of participants (168/195, 86%) reported disruptions to their sleep patterns caused by the COVID-19 pandemic, with over one-third (38%) reporting such disruptions as severe. Half of students who reported some disruption (84/168, 50%) stated that they tended to stay up later or wake up later than they did before the COVID-19 outbreak. Another disruptive impact brought by the pandemic was irregular sleep patterns such as inconsistent time to go to bed and to wake up from day to day (28/168, 17%). Some (12/168, 7%) reported increased hours of sleep, while others (10/168, 6%) had poor sleep quality.

Increased Social Isolation

A majority of participants answered that the pandemic has increased the level of social isolation (167/195, 86%). Over half of these students (91/167, 54%) indicated that their overall interactions with other people such as friends had decreased significantly. In particular, about one-third (52/167, 31%) shared their worries about a lack of in-person interactions such as face-to-face meetings. Others (9/167, 5%) stated that disruptions to their outdoor activities (eg, jogging, hiking) have affected their mental health.

Concerns About Academic Performance

A majority of participants (159/195, 82%) showed concerns about their academic performance being impacted by the pandemic. The biggest perceived challenge was the transition to online classes (61/159, 38%). In particular, participants stated their concerns about sudden changes in the syllabus, the quality of the classes, technical issues with online applications, and the difficulty of learning online. Many participants (36/159, 23%) were worried about progress in research and class projects because of restrictions put in place to keep social distancing and the lack of physical interactions with other students. Some participants (23/159, 14%) mentioned the uncertainty about their grades under the online learning environment to be a major stressor. Others (12/159, 8%) indicated their reduced motivation to learn and tendency to procrastinate.

Disruptions to Eating Patterns

COVID-19 has also negatively impacted a large portion of participants’ dietary patterns (137/195, 70%). Many (35/137, 26%) stated that the amount of eating has increased, including having more snacks since healthy dietary options were reduced, and others (27/137, 20%) addressed that their eating patterns have become inconsistent because of COVID-19, for example, irregular times of eating and skipping meals. Some students (16/137, 12%) reported decreased appetite, whereas others (7/137, 5%) were experiencing emotional eating or a tendency to eat when bored. On the other hand, some students (28/195, 14%) reported that they were having healthier diets, as they were cooking at home and not eating out as much as they used to.

Changes in the Living Environment

A large portion of the participants (130/195, 67%) described that the pandemic has resulted in significant changes in their living conditions. A majority of these students (89/130, 68%) referred to living with family members as being less independent and the environment to be more distractive. For those who stayed in their residence either on- or off-campus (18/130, 14%), a main change in their living environment was reduced personal interactions with roommates. Some (9/130, 7%) mentioned that staying inside longer due to self-quarantine or shelter-in-place orders was a primary change in their living circumstances.

Financial Difficulties

More than half of the participants (115/195, 59%) expressed their concerns about their financial situations being impacted by COVID-19. Many (44/115, 38%) noted that COVID-19 has impacted or is likely to impact their own current and future employment opportunities such as part-time jobs and internships. Some (21/115, 18%) revealed the financial difficulties of their family members, mostly parents, getting laid off or receiving pay cuts in the wake of COVID-19.

Increased Class Workload

The effect of COVID-19 on class workload among the college students was not conclusive. Although slightly over half of participants (106/195, 54%) indicated their academic workload has increased due to COVID-19, the rest stated the workload has remained the same (70/195, 36%) or rather decreased (19/195, 10%). For those who were experiencing increased workloads, nearly half (51/106, 48%) thought they needed to increase their own efforts to catch up with online classes and class projects given the lack of in-person support from instructors or teaching assistants. About one-third of the participants (33/106, 31%) perceived that assignments had increased or became harder to do. Some (6/106, 6%) found that covering the remainder of coursework as the classes resumed after the 2-week break to be challenging.

Depressive Thoughts

When asked about the impact of the COVID-19 pandemic on depressive thoughts, 44% (86/195) mentioned that they were experiencing some depressive thoughts during the COVID-19 pandemic. Major contributors to such depressive thoughts were loneliness (28/86, 33%), insecurity or uncertainty (10/86, 12%), powerlessness or hopelessness (9/86, 10%), concerns about academic performance (7/86, 8%), and overthinking (4/86, 5%).

Suicidal Thoughts

Out of 195 participants, 16 (8%) stated that the pandemic has led to some suicidal thoughts with 5% (10/16) reporting these thoughts as mild and 3% (6/16) as moderate. There were 6 participants (38%) that attributed their suicidal thoughts to the presence of depressive thoughts. Other reasons were related to academic performance (1/16, 6%), problems with family as they returned home (1/16, 6%), and fear from insecurity and uncertainty (1/16, 6%).

Coping Mechanism During COVID-19

To cope with stress and anxiety imposed by COVID-19, college students reported seeking support from others but were mainly using various self-management methods.

Self-Management

The majority of the participants (105/138, 76%) with increased stress due to the outbreak of COVID-19 explained that they were using various means to help themselves cope with stress and anxiety during the pandemic. Some (24/105, 23%) relied on negative coping methods such as ignoring the news about COVID-19 (10/105), sleeping longer (7/105), distracting themselves by doing other tasks (5/105), and drinking or smoking (2/105). Approximately one-third (30/105, 29%) used positive coping methods such as meditation and breathing exercises (18/105), spiritual measures (7/105), keeping routines (4/105), and positive reframing (2/105). A majority of the participants (73/105, 70%) who used self-management mentioned doing relaxing hobbies including physical exercise (31/105), enjoying streaming services and social media (22/105), playing with pets (7/105), journaling (5/105), listening to music (4/105), reading (2/105), and drawing (2/105). Finally, some participants (15/105, 14%) stated that they were planning activities (eg, drafting to-do lists) for academic work and personal matters as a self-distraction method.

Seeking Support From Others

Approximately one-third of the participants (47/138, 34%) mentioned that communicating with their families and friends was a primary way to deal with stress and anxiety during COVID-19. Some explicitly stated that they were using a virtual meeting application such as Zoom frequently to connect to friends and family. Only 1 participant claimed to be receiving support from a professional therapist, and another participant was using Sanvello, a mobile mental health service app provided by the university.

Barriers to Seeking Professional Support During COVID-19

Despite the availability of tele-counseling and widespread promotion of such services by the university, a vast majority of participants who indicated an increase in stress and anxiety (128/138, 93%) claimed that they had not used school counseling services during the pandemic. Reasons for such low use included the condition not being perceived as severe enough to seek the services (4/128, 3%), not comfortable interacting with unfamiliar people (1/128, 0.8%), not comfortable talking about mental health issues over the phone (1/128, 0.8%), and lack of trust in the counseling services (1/128, 0.8%).

Principal Findings

College students comprise a population that is considered particularly vulnerable to mental health concerns. The findings of this study bring into focus the effects of pandemic-related transitions on the mental health and well-being of this specific population. Our findings suggest a considerable negative impact of the COVID-19 pandemic on a variety of academic-, health-, and lifestyle-related outcomes. By conducting online survey interviews in the midst of the pandemic, we found that a majority of the participants were experiencing increased stress and anxiety due to COVID-19. In addition, results of the PSS showed moderate levels of stress among our participants. This is in line with a recent pre–COVID-19 survey conducted in the United Kingdom (mean PSS score 19.79, SD 6.37) [ 28 ]; however, the administration of PSS as interview questions (compared to allowing participants to read and respond to the 10 questions) might have introduced bias and resulted in underreporting.

Among the effects of the pandemic identified, the most prominent was worries about one’s own health and the health of loved ones, followed by difficulty concentrating. These findings are in line with recent studies in China that also found concerns relating to health of oneself and of family members being highly prevalent among the general population during the pandemic. Difficulty in concentrating, frequently expressed by our participants, has previously been shown to adversely affect students’ confidence in themselves [ 29 ], which has known correlations to increased stress and mental health [ 30 ]. In comparison with stress and anxiety in college students’ general life, it appears that countermeasures put in place against COVID-19, such as shelter-in-place orders and social distancing practices, may have underpinned significant changes in students’ lives. For example, a vast majority of the participants noted changes in social relationships, largely due to limited physical interactions with their families and friends. This is similar to recent findings of deteriorated mental health status among Chinese students [ 10 ] and increased internet search queries on negative thoughts in the United States [ 31 ]. The findings on the impact of the pandemic on sleeping and eating habits are also a cause for concern, as these variables have known correlations with depressive symptoms and anxiety [ 20 ].

Although a majority of participants expressed concerns regarding academic performance, interestingly, almost half of the participants reported lower stress levels related to academic pressure and class workload since the pandemic began. This may be due, in part, to decisions taken by professors and the university to ease the students’ sudden transition to distance learning. For instance, this university allowed students to choose a pass/fail option for each course instead of a regular letter grade. Additionally, actions taken by professors, such as reduced course loads, open book examinations, and other allowances on grading requirements, could also have contributed to alleviating or reducing stress. Although participants who returned to their parental home reported concerns about distractions and independence, students might have benefited from family support and reduced social responsibilities. Therefore, the increased stress due to the pandemic may have been offset, at least to some extent.

Alarmingly, 44% (86/195) of the participants reported experiencing an increased level of depressive thoughts, and 8% (16/195) reported having suicidal thoughts associated with the COVID-19 pandemic. Previous research [ 32 ] reported about 3%-7% of the college student population to have suicidal thoughts outside of the pandemic situation. Furthermore, with the exception of high-burnout categories, depression levels among students, reported in several recent studies [ 33 - 35 ], have varied between 29% and 38%, which may suggest an uptick in pandemic-related depressive symptoms among college students similar to recent studies in China [ 10 , 11 ]. Although our participants specifically mentioned several factors such as feelings of loneliness, powerlessness, as well as financial and academic uncertainties, other outcomes that were perceived to be impacted by the COVID-19 pandemic may also act as contributors to depressive thoughts and suicidal ideation. In particular, both difficulty concentrating and changes in sleeping habits are associated with depression [ 20 , 29 , 36 ].

Our study also identifies several coping mechanisms varying between adaptive and maladaptive behaviors. The maladaptive coping behaviors such as denial and disengagement have been shown to be significant predictors of depression among young adults [ 37 ]. In contrast, adaptive coping such as acceptance and proactive behaviors are known to positively impact mental health. Our findings suggest that the majority of our participants exhibited maladaptive coping behaviors. Identifying students’ coping behavior is important to inform the planning and design of support systems. In this regard, participatory models of intervention development can be used, in which researchers’ and psychologists’ engagement with the target population to adapt interventional programs to their specific context has shown promise [ 37 , 38 ]. For instance, Nastasi et al [ 37 ] used a participatory model to develop culture-specific mental health services for high school students in Sri Lanka. Similar approaches can be adopted to engage college students as well to develop a mental health program that leverages their natural positive coping behaviors and addresses their specific challenges.

Participants described several barriers to seeking help, such as lack of trust in counseling services and low comfort levels in sharing mental health issues with others, which may be indicative of stigma. Perceiving social stigma as a barrier to seeking help and availing counseling services and other support is common among students [ 29 ]. One study showed that only a minor fraction of students who screened positive for a mental health problem actually sought help [ 39 ]. Although overcoming the stigma associated with mental health has been discussed at length, practical ways of mitigating this societal challenge remains a gap [ 40 , 41 ]. Our findings suggest that self-management is preferred by students and should be supported in future work. Digital technologies and telehealth applications have shown some promise to enable self-management of mental health issues [ 42 ]. For instance, Youn et al [ 43 ] successfully used social media networks as a means to reach out to college students and screen for depression by administering a standardized scale, the Patient Health Questionnaire-9. Digital web-based platforms have also been proposed to enhance awareness and communication with care providers to reduce stigma related to mental health among children in underserved communities [ 44 ]. For instance, one of the online modules suggested by the authors involves providing information on community-identified barriers to communicating with care providers. Technologies such as mobile apps and smart wearable sensors can also be leveraged to enable self-management and communication with caregivers.

In light of the aforementioned projections of continued COVID-19 cases at the time of this writing [ 45 ] and our findings, there is a need for immediate attention to and support for students and other vulnerable groups who have mental health issues [ 17 ]. As suggested by a recent study [ 46 ] based on the Italian experience of this pandemic, it is essential to assess the population’s stress levels and psychosocial adjustment to plan for necessary support mechanisms, especially during the recovery phase, as well as for similar events in the future. Although the COVID-19 pandemic seems to have resulted in a widespread forced adoption of telehealth services to deliver psychiatric and mental health support, more research is needed to investigate use beyond COVID-19 as well as to improve preparedness for rapid virtualization of psychiatric counseling or tele-psychiatry [ 47 - 49 ].

Limitations and Future Work

To our knowledge, this is the first effort in documenting the psychological impacts of the COVID-19 pandemic on a representative sample of college students in the United States via a virtual interview survey method in the middle of the pandemic. However, several limitations should be noted. First, the sample size for our interview survey was relatively small compared to typical survey-only studies; however, the survey interview approach affords the capture of elaboration and additional clarifying details, and therefore complements the survey-based approaches of prior studies focusing on student mental health during this pandemic [ 10 , 11 , 50 ]. Second, the sample used is from one large university, and findings may not generalize to all college students. However, given the nationwide similarities in universities transitioning to virtual classes and similar stay-at-home orders, we expect reasonable generalizability of these findings. Additionally, a majority of our participants were from engineering majors. Therefore, future work is needed to use a stratified nationwide sample across wider disciplines to verify and amend these findings. Third, although a vast majority of participants answered that they have not used the university counseling service during the pandemic, only a few of them provided reasons. Since finding specific reasons behind the low use is a key to increasing college students’ uptake of available counseling support, future research is warranted to unveil underlying factors that hinder college students’ access to mental health support. Finally, we did not analyze how student mental health problems differ by demographic characteristics (eg, age, gender, academic year, major) or other personal and social contexts (eg, income, religion, use of substances).

Future work could focus on more deeply probing the relationships between various coping mechanisms and stressors. Additionally, further study is needed to determine the effects of the pandemic on students’ mental health and well-being in its later phases beyond the peak period. As seen in the case of health care workers in the aftermath of the severe acute respiratory syndrome outbreak, there is a possibility that the effects of the pandemic on students may linger for a period beyond the peak of the COVID-19 pandemic itself [ 51 ].

Acknowledgments

This research was partly funded by a Texas A&M University President’s Excellence (X-Grant) award.

Conflicts of Interest

None declared.

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Abbreviations

Edited by G Eysenbach, G Fagherazzi, J Torous; submitted 10.06.20; peer-reviewed by T Liu, V Hagger; comments to author 28.07.20; revised version received 01.08.20; accepted 15.08.20; published 03.09.20

©Changwon Son, Sudeep Hegde, Alec Smith, Xiaomei Wang, Farzan Sasangohar. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.09.2020.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

• Open access
• Published: 17 December 2022

University students’ use of mental health services: a systematic review and meta-analysis

• T. G. Osborn 1 ,
• R. Saunders 1 , 2 &
• P. Fonagy 1  

International Journal of Mental Health Systems volume  16 , Article number:  57 ( 2022 ) Cite this article

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International estimates suggest around a third of students arrives at university with symptoms indicative of a common mental disorder, many in late adolescence at a developmentally high-risk period for the emergence of mental disorder. Universities, as settings, represent an opportunity to contribute to the improvement of population mental health. We sought to understand what is known about the management of student mental health, and asked: (1) What proportion of students use mental health services when experiencing psychological distress? (2) Does use by students differ across health service types?

A systematic review was conducted following PRISMA guidelines using a Context, Condition, Population framework (CoCoPop) with a protocol preregistered on Prospero (CRD42021238273). Electronic database searches in Medline, Embase, PsycINFO, ERIC and CINAHL Plus, key authors were contacted, citation searches were conducted, and the reference list of the WHO World Mental Health International College Student Initiative (WMH-ICS) was searched. Data extraction was performed using a pre-defined framework, and quality appraisal using the Joanna Briggs Institute tool. Data were synthesised narratively and meta-analyses at both the study and estimate level.

7789 records were identified through the search strategies, with a total of 44 studies meeting inclusion criteria. The majority of included studies from the USA (n = 36), with remaining studies from Bangladesh, Brazil, Canada, China, Ethiopia and Italy. Overall, studies contained 123 estimates of mental health service use associated with a heterogeneous range of services, taking highly variable numbers of students across a variety of settings.

This is the first systematic quantitative survey of student mental health service use. The empirical literature to date is very limited in terms of a small number of international studies outside of the USA; studies of how services link together, and of student access. The significant variation we found in the proportions of students using services within and between studies across different settings and populations suggests the current services described in the literature are not meeting the needs of all students.

Globally, university students could be considered a privileged group given the significant variation in percentage of national populations with a university education [ 1 ]. However, for those who do attend university usually do so at a developmentally high risk period for the emergence of mental heath problems [ 2 , 3 ]. Psychological distress, encompassing symptoms ranging from normal fluctuations in mood to the emergence of a serious mental illness, is an increasingly common experience among university students which can have significant consequences for individuals [ 4 , 5 ]. Recent international evidence suggests 35% of first year students report symptoms indicative of lifetime mental disorder, and 31.4% report symptoms in the previous 12 months [ 6 ]. International longitudinal research is more limited. Studies in Norway, the UK and the USA has shown both psychological distress and common mental disorders (CMD) have increased in prevalence among both students and similar aged non-student populations over the last 10 years [ 7 , 8 , 9 , 10 , 11 ]. Suicidal behaviour, while lower in students compared to matched non-student populations, has also increased over a similar timeframe in England and Wales [ 12 ]. International estimates among students suggest around 4.3% have attempted suicide in their lifetime [ 6 ]. The short- and longer-term consequences of mental health difficulties can be significant including poorer academic performance, relationship breakdown, and exclusion from the labour market [ 6 , 13 , 14 ]. Current students face greater financial and academic pressures compared to 20 years ago, which may be contributing to poorer mental health outcomes [ 2 , 15 , 16 , 17 ]. These findings suggest a significant mental health need among this population. [ 1 ].

For students in mental distress, the support available to them is likely to vary signficiantly between and within countries. For example, in many high-income countries (HIC) students may have a range of effective mental health services available to them but these services are often fragmented, uncoordinated and underutilised [ 6 , 19 , 20 ]. For example, US studies suggest around a 1/3 of students received treatment [ 9 ], while epidemiological studies suggest this varies widely independent of need based on sex and gender, ethnicity, age, and where they attend university [ 6 , 20 , 21 , 22 , 23 ]. Barriers such as self-stigma, perceived need, and self-reliance influence when and how they seek help, while student’s also report a lack of awareness of appropriate services, concerns about confidentiality and discrimination, cost, or may perceive services to be ineffective or inappropriate [ 19 , 24 , 25 ]. These barriers may explain why some students only seek help in crisis and others tend to rely on informal sources of support [ 26 , 27 ]. International studies suggest very few students with need, receive support globally. One recent international cross-sectional study found 19.8% of first year university students, and 36% of those who may meet criteria for CMD report having ever used a mental health service, defined as medication or psychological counselling [ 6 ]. Compared to HICs, much less is known about students in Lower and Middle Income Countries (LMIC), although individual studies suggest very small numbers of students report accessing support when in distress [ 18 , 28 ].

While a limited number of studies have highlighted the scale and nature of the problem outside of the USA, there is a renewed effort to understand and address barriers to treatment that stop some students reaching help in the first place [ 4 , 16 , 27 ]. The World Health Organization’s (WHO) World Mental Health International College Student Initiative (WMH-ICS) aims to provide greater clarity on the unmet need of this group [ 16 ]. In the UK, there has been a policy focus on improving access to mental health interventions through greater integration between the National Health Service (NHS) and Universities, and an emphasis on mobilising university resources towards the mental health of students [ 29 , 30 ]. Previous reviews in the USA have looked at which students are most likely to seek help [ 20 , 31 ], however this is obviously confounded by the nature of services available to them. There are no systematic reviews conducted on the variety of services available to students internationally, how these integrate with each other and how use varies by types of service that deliver interventions to support mental health and wellbeing. Studies have examined individual services such as university counselling centres, external psychological services, or inpatient settings but have not compared the differential use of these by students with different clinical presentations. Given the developmental period in which many students attend university these settings are important in contributing to improving overall population mental health [ 3 , 32 ]. By understanding where variation occurs could indicate areas of differential access, highlighting where care pathways could be improved and inform policy initiatives.

This systematic review was conducted to address this gap, by answering two review questions: (1) what proportion of university students use mental health services when experiencing psychological distress? And (2) does utilisation differ across health service type?

This review was reported in accordance with PRISMA guidelines [ 33 ] (see Additional file 1 : Appendix S1). A protocol for this review was pre-registered on the 22/02/21 on PROSPERO ( https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021238273 ).

Deviations from initial protocol

On the 26th of April 2021 we made an amendment to only include studies published in the year 2000 or after over concerns around changes to the student population that would create issues of comparability [ 4 ]. On the 27th of July 2021 we amended the focus of the review as the original aims were considered too broad for a coherent synthesis. The amendment removed one review question related to student characteristics associated with service use which could be explored in future analysis.

Eligibility criteria

Studies were included that:

Measured the use or utilisation of mental health services (as a primary or secondary outcome).

Studies that included adults (aged 18 +) studying at a university.

Studies were excluded:

That employed an empirical study design that aimed to test an intervention or approach to address or effect access or use of healthcare services.

Where it was not possible to extract sociodemographic and utilisation data for student participants.

Where participants under 18 were recruited.

Where participants weren’t all university students.

Studies needed to be published in English due to the languages spoken by the primary reviewer (TO).

Search strategy

The following electronic databases were searched on the 9th of March 2021, 3rd of November 2021 and the 23rd of August 2022: MEDLINE (Ovid); EMBASE (Ovid); PsycINFO (Ovid); ERIC (ESBCO); and CINAHL plus (ESBCO). The search strategy using a Context, Condition, Population (CoCoPop) framework with the concepts of “students”, “mental health/illness”, “access” and “mental health services” [ 34 ]. Key words and MeSH terms were developed in Medline between 2nd of December 2020 and 9th of March 2021, and adapted for each database (see Additional file 1 : Appendix S2). On the 16th and 17th of June 2021, the 14th of December 2021 and the 16th of November 2022 forward and backward citation searching was conducted. The publicly available reference list of studies published by the WHO’s WMH-ICS was searched on the 23rd of April 2021, the 14th of December 2021 and the 16th of November 2022. The authors of the originally included studies were contacted on the 18th of June 2021, where possible, to help identify any unpublished or ongoing research.

Data extraction

Records retrieved from electronic database searches were exported to Endnote X9, where duplicates were removed. Abstracts and full texts of potentially relevant articles were screened against the inclusion and exclusion criteria on Rayyan software. A random sample of approximately 10% of titles and abstracts identified in the initial searches were screened independently by a second reviewer (SL) using a purpose designed screening tool (see Additional file 1 : Appendix S3). Data from the included studies were extracted independently by two reviewers (TO and SL) using a pre-defined data extraction framework (see Additional file 1 : Appendix S4). Data were extracted into Excel. After data were extracted for two studies, the data extraction framework was checked for interpretation by both TO and SL. Study authors were contacted where additional data or clarification was required. The main items of interest were:

i Condition: use or utilisation

We defined use as the occurrence or number of uses of a mental health service over a defined time-period [ 35 ]. Indicators could include attendances, usage, inpatient days, admissions, contacts, episodes, or costs due to the receipt of treatment or attendance [ 35 ]. These indicators may be measured through self-report, clinical records, and/ or other routinely collected data. As observational or more naturalistic study designs were included in this review, outcomes are likely to be reported as prevalence or incidence and therefore as a proportion of the total study sample. Therefore, the effect measures were proportions with a 95% confidence interval as the main outcome [ 34 ].

ii Context: mental health service

An amended version of the WHO’s definition of a mental health service was used, this being ‘the means by which effective interventions are delivered for the dominant or subdominant intention to improve wellbeing or mental health’ [ 36 ]. This included outpatient services, day treatment, inpatient wards, community mental health teams, General Practice, mental health hospitals, and university counselling services [ 36 ]. To facilitate comparison of proportions by service type an adapted version of the Description and Evaluation of Services for Disabilities in Europe (DESDE) instrument was used (see Appendix S5) [ 37 ]. This is a hierarchical classification system, with six initial categories: (1) Information for care, (2) Accessibility to care, (3) Self-help and volunteer care, (4) Outpatient Care, (5) Day care, and (6) Residential care. A random 10% sample were double coded by two reviews (TO and SL). No service descriptions could be classified beyond the first level of the DESDE hierarchy. Therefore, to further specify, we used the National Institute for Health and Care Excellence (NICE) treatment stepped care categories, referred to as ‘treatment type’ [ 38 ], and the service location—being either on campus, off campus, or potentially either.

iii Other items

We also collected sociodemographic characteristics, study design, duration of study, data collection methods, data analysis methods, setting and date of study, raw data for the outcome, indicator(s) used, and time point(s) outcomes where reported, source of funding and conflicts of interest.

Quality assessment

We assessed risk of bias using the Joanna Briggs Institute (JBI) appraisal checklist for systematic review reporting prevalence data [ 34 ]. The checklist prompts the reviewer to answer nine questions with four possible response options: “yes”/ “no”/ “unclear”/ “not applicable”. Each study was assigned low, moderate, or high quality based on the number of yes answers it scored to indicate study quality. Studies with 1–3 ‘yes’ were low, 3–6 indicating moderate, and 7–9 as high quality. Quality appraisal was conducted independently on all studies meeting the inclusion criteria by two reviewers (TO and SL). Where there were disagreements, these were discussed until agreement was reached. No studies were excluded based on the study quality to enable sensitivity analyses to be conducted by removing studies rated as low quality.

Synthesis methods

I narrative synthesis.

Initially, a non-statistical narrative synthesis was conducted to describe the included studies relevant to the review questions [ 34 ]. Study participants and the measures of psychological symptoms were not universally well described. Therefore, the samples were qualitatively summarised and then categorised based on whether this was a general student sample, subgroup sample or a sample of students with more severe current psychological distress, referred to as ‘at risk’.

ii Meta-analysis

Most studies provided data for multiple service types, therefore three-level mixed effects models were used to account for clustering. Where the study provided a single estimate or an overall estimate of service use they were included in one of three conventional random effects meta-analytic models: (1) overall service use (any service), (2) overall outpatient service use, (3) overall residential service use reflecting the service types commonly observed in the data. Following this, to specifically test differences between these service types all estimates were then included into a three-level mixed effects model, where sub-group analysis and meta-regression were also conducted [ 39 ]. Further analyses were conducted for studies providing multiple estimates within the same study using two three-level mixed effects models to account for clustering: (1) outpatient service use; (2) service use where the service could be classed within multiple DESDE service categories.

For all pooled proportions, a priori subgroup analysis and meta-regression were conducted based on population group. Post-hoc analyses were conducted based on service location, treatment type, reporting timeframes, publication year, study design, and country, due to the substantial estimated heterogeneity. To conduct meta-regression for recall time-period a continuous variable was created based on the number of months participants were asked to recall service use (e.g., 12 months). If the reporting time-period did not use months (e.g., the student’s lifetime), it was estimated using the average age of the participants.

Heterogeneity was further explored by identifying outliers above or below the 95% confidence interval of the pooled proportion; by conducting influencer analysis; drafting a Baujat plot and conducting Graphic Display of Heterogeneity (GOSH) plots [ 39 ].

Sensitivity analyses were conducted for pooled estimates where low quality studies, estimates of lifetime service use and outliers and influential cases were excluded then all described analyses were repeated. Publication bias was not assessed due to the substantial between study heterogeneity [ 39 ].

Search results

A total of 7739 unique titles / abstracts were identified through database searches, and a further 52 through other search strategies (see Fig.  1 and Additional file 1 : Appendix S6). Inter-rater agreement for data screening was Cohen’s Kappa ( K ) = 0.85 indicating strong agreement [ 40 ].

PRISMA flow diagram

As a result of these search strategies, 44 studies were deemed eligible for inclusion. Within these studies there were 123 estimates of service use. Seven of these studies were smaller analyses of larger surveys conducted in the USA [ 23 , 41 , 42 , 43 , 44 , 45 , 46 ]. These seven studies were excluded from meta-analysis as their estimates would double count participants. 29 studies and 42 estimates were included in conventional two-level meta-analyses pooling estimates of overall service use, and then a three-level meta-analysis to test differences by service type. 25 studies and 60 estimates were included in further analyses using three-level meta-analysis. Inter-rater agreement for data extraction was K  = 0.82 indicating strong agreement [ 40 ].

Study characteristics

I study origin.

Studies were conducted in a range of mostly high-income countries. The majority were from the United States, where 34 of the 44 studies were based [ 9 , 23 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 ]. The remainder from Australia [ 73 , 74 ], Brazil [ 75 , 76 ], China [ 77 ], Canada [ 78 ], Ethiopia [ 79 ], Bangladesh [ 28 ], and Italy [ 80 ]. A total of nineteen studies were samples of students from separate individual universities [ 43 , 46 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 67 , 68 , 70 , 73 , 75 , 76 , 77 , 79 , 80 ]. Whereas the remaining twenty-four were samples across multiple universities [ 9 , 20 , 23 , 28 , 41 , 44 , 45 , 47 , 56 , 57 , 58 , 59 , 61 , 62 , 63 , 64 , 65 , 66 , 69 , 71 , 72 , 74 , 78 ].

ii Study design and methods

Most studies (n = 36) were either primary or secondary analyses of cross-sectional surveys [ 9 , 20 , 23 , 41 , 43 , 44 , 45 , 47 , 49 , 50 , 51 , 53 , 54 , 55 , 56 , 58 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 73 , 74 , 75 , 78 , 79 ] (see Table 1 ). Outcomes were assessed using standardised questionnaires and open questions. Of the remaining seven studies, one was a longitudinal study [ 46 ], one was a cohort study using a mix of a baseline survey and linked electronic medical records from the university counselling centre [ 77 ], two were secondary data analyses of electronic medical records from university counselling or health centres [ 52 , 59 , 60 ], and two were mixed method studies [ 48 , 80 ].

iii Study participants

Sample sizes varied substantially ranging from 15 to 730,785 participants. Most studies included general samples of student attending a university with fifteen studies studying specific subgroups of students [ 41 , 44 , 51 , 52 , 58 , 59 , 61 , 63 , 65 , 69 , 70 , 71 , 73 , 74 , 75 , 76 ]. Thirteen studies included samples of students ‘at risk’ [ 23 , 48 , 49 , 50 , 56 , 57 , 62 , 64 , 66 , 68 , 72 , 79 , 80 ]. Two studies sampled university faculty members, in addition to university students, although these participants were not asked about mental health service use [ 41 , 47 ]. One study included students at community college and 4-year institutions in the USA [ 23 ].

iv Mental health services

Overall, most estimates were associated with services classified into the outpatient service category of the DESDE instrument (see Table 2 ). Seventy-four estimates associated with thirty-seven studies were outpatient services [ 9 , 20 , 28 , 41 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 55 , 57 , 59 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 70 , 71 , 72 , 73 , 75 , 76 , 77 , 78 , 79 , 80 ]. Thirty-seven estimates associated with twenty-two studies could be classed as multiple service categories [ 9 , 20 , 23 , 41 , 47 , 50 , 53 , 56 , 57 , 61 , 62 , 63 , 64 , 65 , 66 , 68 , 69 , 70 , 71 , 74 , 78 ]. Residential service category was appropriate for seven estimates associated with five studies [ 9 , 57 , 61 , 66 , 70 ]. Inter-rater agreement for service coding was Κ  = 0.89, indicating strong agreement [ 40 ].

Across the service categories, 38 estimates related to services providing a range of treatments, 1 providing advice and support, 25 providing low intensity treatment, 35 related to high intensity treatment and 17 related to specialist treatment. Of these estimates thirteen related to services located off campus; 29 were on campus, whereas the remaining 79 estimates could have been located on or off a university campus.

v Defining and measuring use of health services

While all studies implicitly conceptualised mental health service use as an event or occurrence by a person in a time-period, the operational assessment was heterogeneous. In the cross-sectional and longitudinal studies, measurement varied by recall period and by item wording [ 9 , 20 , 23 , 28 , 41 , 43 , 44 , 45 , 47 , 49 , 50 , 51 , 53 , 54 , 55 , 56 , 58 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 78 , 79 ]. Only one study used a validated instrument assessing use over the previous two weeks [ 79 ], one asked student about their use over the previous two months [ 49 ], sixteen over the last 12 months [ 9 , 23 , 28 , 42 , 43 , 44 , 45 , 46 , 50 , 56 , 57 , 58 , 67 , 70 , 72 , 74 ], four while students were at university [ 41 , 47 , 68 , 71 ], and ten asked participants to report about previous use in their lifetime or ever [ 55 , 61 , 62 , 63 , 64 , 65 , 66 , 69 , 78 ]. One cross-sectional study asked student participants to both recall use of university counselling centre while at university, and the students use of other mental health service over their lifetime [ 66 ]. Nearly all cross-sectional studies gave participants a binary response option—either yes or no. Only one study used an ordered categorical response option where participants were asked to state whether they had used a particular service using a Likert scale ranging from 1–5 (never-often) [ 50 ]. Of the two mixed methods studies one reported current use [ 48 ], and the other reported on lifetime use [ 80 ]. Secondary analyses of electronic medical records examined number of unique visits per student over the study period [ 52 , 59 , 60 ].

Quality appraisal

Overall, the quality of the studies included in the review were moderate with around a quarter of the total samples rated as either high [ 43 , 44 , 45 , 46 , 56 , 67 , 72 , 79 ], or low quality [ 49 , 52 , 54 , 61 , 65 , 69 , 76 ]. The main area of weakness came from questions related to the validity and reliability of the assessment of mental health service use, with only six studies being rated as “yes” in both questions [ 45 , 46 , 56 , 67 , 74 , 79 ]. A further area of significant weakness was found in question eight which related to whether appropriate statistical analyses had been conducted with four studies rated as “yes” [ 49 , 53 , 59 , 63 ] (see Table 1 and Additional file 1 : Appendix S7). Inter-rater agreement for quality appraisal was Κ  = 0.88 indicating strong agreement [ 40 ].

What proportion of university students use mental health services when experiencing psychological distress?

I. overall use of any mental health service, narrative summary (n = 10; k = 11).

Ten studies reporting on students’ use of any mental health service use with estimates ranging between 13.7 and 68.6% of the study population reporting use [ 9 , 41 , 47 , 50 , 53 , 57 , 61 , 64 , 70 , 71 , 74 , 78 ]. Estimates ranged from 13.7 to 68.6% of the study population reporting using a service. It was difficult conclude the source of this variation. The highest estimate, at 68.6%, was the only for an on-campus service. Treatment offered by the service did not appear to be associated with variation across estimates. Broader operational service definitions tended to have higher estimates [ 53 , 74 ]. For example, in one study 49% of Chinese international students reported using “any form of help”, whereas all other estimates within the same study relating to specific services were low.

There was some evidence to suggest more severe current psychological distress was associated with higher previous mental health service use. For example, in studies with at risk samples reported estimates between 25.7 and 49% [ 50 , 57 , 74 ]. Whereas estimates in general populations of students had a lower range between 19.7 and 45% [ 9 , 47 , 53 , 78 ]. Variation also appeared to be related to the reporting period, where studies reporting on lifetime mental health service use tended to have higher estimates [ 61 , 78 ] (see Tables 1 and 2 ).

Meta-analysis (n = 9; k = 9)

The overall pooled proportion effect size using a random effects model was estimated to be 0.35 (95%CI: 0.22;0.50) (see Fig.  2 ). The between study heterogeneity was estimated at τ 2  = 0.69, and Ι 2  = 99.9%. The prediction interval ranged from 0.06 to 0.81. This indicated a wide range of future possible estimates. Overall, these results indicate substantial heterogeneity across the included estimates of mental health service use.

Forest plot for overall mental health service use by population group

Subgroups and meta-regressions for overall use

No variables were associated with an overall reduction in between study heterogeneity using meta-regressions. Subgroup analyses found differences by service location ( Q  = 40.41, df:2, p  < 0.001), and reporting period ( Q  = 5.92, df:2, p  = 0.05), However, meta-regressions found lower proportions were associated with off-campus service ( β  = − 1.35, 95%CI:− 2.52; − 0.18, p  =  0 .03), and higher proportions associated with longer reporting periods ( β  = 0.0043, 95%CI:− 0.001; 0.0075, p  = 0.02) (see Additional file 1 : Appendix S8).

ii Overall outpatient use

Narrative summary (n = 25; k = 27).

Twenty-five studies reported estimates of students overall outpatient service use with between 2.6 and 75% of the study populations reporting service use [ 9 , 28 , 41 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 57 , 59 , 61 , 62 , 63 , 66 , 67 , 69 , 70 , 71 , 72 , 73 , 75 , 76 , 77 , 80 ]. Use of on-campus services were lower ranging between 2.6 and 33.5% [ 9 , 41 , 47 , 50 , 51 , 52 , 58 , 59 , 60 , 66 , 69 , 73 , 77 ]. There was only one estimate of off-campus service use at 13.7% [ 49 ], whereas the remaining estimates were for services that could be either on or off campus between 7 and 75%. These differences could also be partly explained by differences in population group and treatment offered by the service. The lowest two estimates overall were in subgroups of students namely international students (2.6%) [ 52 ], and students in China (5.1%) [ 77 ], and among students Bangladeshi universities (7.1%) [ 28 ]. Whereas the highest estimates overall and in the category of either on campus or off campus services were in a study of medical students with more severe current psychological distress using services offering potentially any treatment (75%) [ 73 ]; previously homeless students or who had been in care where a broad service model had been developed for them (68%) [ 48 ], and veterinary students (62.5%) [ 61 ]. For this estimate participants reported against the use of “counselling”—which could have a broad interpretation in the USA. A further study also using a broad outpatient service definition was associated with a high estimate of 68% [ 49 ]. Overall, studies asking students to recall service use over their lifetime reported a higher range of estimates [ 61 , 62 , 63 , 69 , 80 ], compared to studies with shorter recall periods (see Tables 1 and 2 ).

Meta-analysis for overall outpatient use (n = 24; k = 26)

The overall pooled proportion effect size using a random effects model was estimated to be 0.21 (95%CI = 0.15;0.30) (see Fig.  3 ). The between study heterogeneity was estimated at τ 2  = 1.12 and Ι 2  = 99.9%. The prediction interval ranged from 0.03 to 0.72. This indicated a wide range of future possible estimates. Overall, these results indicate substantial heterogeneity across the included estimates of residential mental health service use.

Forest Plot for outpatient overall service use by population group

Sub-group analyses and meta-regressions for overall outpatient use

No meta-regression model resulted in a significant reduction in overall between-study heterogeneity. Subgroup analyses found overall differences by service location ( Q  = 9.03, df:1, p  = 0.002), population group ( Q  = 35.40, df:2, p  < 0.001), study design ( Q  = 94.68, df:3, p  < 0.001) (see Additional file 1 : Appendix S9). Meta-regressions were conducted finding lower proportions of service utilisation were associated with service providing low intensity treatment ( β  = − 0.91; 95%CI = − 1.78;− 0.04; p  = 0.04), and on campus services compared than those either on or off campus ( β  = − 1.10, 95%CI: − 1.85; − 0.36, p  = 0.005). Higher proportions of use were associated in ‘at risk’ to general populations of students ( β  = 1.62, 95%CI:0.88; 2.37, p  < 0.001), and mixed methods studies ( β  = 2.41, 95%CI:0.08; 4.73, p  = 0.04).

iii Overall residential service use

Narrative summary (n = 5; k = 7).

Four studies reported six estimates of residential service use [ 9 , 57 , 61 , 66 , 70 ], ranging from 1 to 5.4%. Population group appeared to be associated with this variation, with the study reporting on general populations of students having a lower estimate than other groups (see Tables 1 and 2 , and Additional file 1 : Appendix S10 for a detailed narrative summary).

Meta-analysis for overall residential service use (n = 5; k = 7)

The overall pooled proportion effect size using a random effects model was estimated to be 0.03 (95%CI:0.02;0.05) (see Fig.  4 ). The between study heterogeneity was estimated at τ 2  = 0.30, and Ι 2  = 99.4%. There was a prediction interval which ranged from a proportion of 0.007 to 0.12. This indicated a wide range of future possible estimates. Overall, these results indicate substantial heterogeneity across the included estimates of residential mental health service use.

Forest Plot for overall residential service use

Subgroup analyses and meta-regressions for overall residential service use

Meta-regressions only a found a reduction in between study heterogeneity association with population group (τ 2  = 0.19, Ι 2  = 86.6%). High estimates were associated with ‘at risk’ students ( β  = 1.29, 95%CI: 0.84; 1.73, p  = 0.001), and subgroup of students ( β  = 1.50, 95%CI: 0.80; 2.21, p  = 0.0041) when compared to general populations of students (see Additional file 1 : Appendix S10).

Does service use differ across health service type?

I differences in use by service type.

Subgroup analysis conducted using a three-level meta-analysis suggested differences between service types ( F  = 63.25, df:2,39, p  < 0.001). A meta-regression was conducted where compared to overall service use, both overall outpatient service and overall residential service use was associated with lower proportion of university students reporting using these services (outpatient: β  = − 0.77, 95%CI: − 1.26; − 0.29; p  = 0.01; residential: β  = − 3.05, 95%CI: − 3.63; − 2.47, p  < 0.001).

Sensitivity analyses found mixed results (see Table 3 ). For example, excluding estimates of lifetime service use had an attenuating effect on all pooled proportions, whereas removing low quality studies resulted in a lower pooled proportion only in overall service use. When outliers and influential estimates were removed the pooled proportion for overall service use was higher. A reduction in between study heterogeneity was only observed when outliers and influential cases were removed (see Table 3 ). Sensitivity analyses continued to suggest differences by service location and treatment type for overall outpatient service use, by service location for overall service use, except when excluding estimates of lifetime use (see Additional file 1 : Appendix S11, 12 and 13).

Further analyses using three-level meta-analysis

I estimates meeting multiple service categories, narrative summary (n = 12; k = 23).

Twelve studies reported on twenty-one estimates associated with services that could be classified as any DESDE classifications [ 9 , 47 , 53 , 55 , 56 , 62 , 63 , 64 , 65 , 70 , 74 , 78 ]. These estimates ranged from 5 to 68%. Lower estimates were reported in services offering specialist or high intensity treatment compared to a range of treatments, whereas higher estimates tended be in campus services. In general, studies asking students report service use over their lifetime were associated with higher estimates [ 55 , 62 , 63 , 64 , 65 , 78 ] (see Tables 1 and 2 ).

Meta-analysis (n = 12; k =  23)

The pooled proportion based on the three-level meta-analytic model was 0.20 (95%CI:0.13; 0.31, p < 0.001). Ι 2 level 3  = 82.9% of the total variation can be attributed to between-cluster, and  Ι 2 level 2  = 13.76% to within-cluster heterogeneity. We found that the three-level model provided a significantly better fit compared to a two-level model with level 3 heterogeneity constrained to zero (χ 2 1  = 8.10, p 0.004).

Subgroup analyses and meta-regressions

Subgroup analyses found differences by service location ( F  = 11.201, df:2,18, p  < 0.001). Meta regressions found on campus, and off campus location was associated with a high proportion when compared service potentially located in both locations (On campus: β  = 1.83, 95%CI:0.83, 2.83, p  = 0.001; off campus: β  = 0.91, 95%CI:0.003, 1.81, p  = 0.05) (see Additional file 1 : Appendix S14, and Appendix S16 for sensitivity analyses).

ii Specific outpatient services

Narrative summary (n = 13; k = 37).

Between 6.98% and 62.5% of students reporting outpatient service use out of the ten studies and twenty-seven estimates [ 49 , 55 , 61 , 64 , 65 , 66 , 67 , 68 , 70 , 71 , 76 , 79 ]. These estimates were between 6.98% and 62.5% of the study populations reporting outpatient service use. It was difficult to determine what this variation was associated with. The definitions used to measure service use may explain some variation. For example, the highest estimate of 62.5% related to individual counselling, and lowest estimate of 6.98% related to group counselling within the same study, and both classed as low intensity treatments [ 61 ]. The country a service was located appeared to potentially be associated with some variation. Estimates in a study of students at risk in Ethiopia were both low compared to most other estimates in the USA [ 79 ]. In general, higher estimates tended to be in studies asking students to report whether they had ever used a mental health service [ 49 , 55 , 61 , 64 , 65 , 68 , 78 ].

Meta-analysis (n = 13; k = 37)

The pooled proportion based on the three-level meta-analytic model was 0.19 (95%CI:0.13; 0.28, p  < 0.001). Ι 2 level 3  = 31.3% of the total variation can be attributed to between-cluster, and  Ι 2 level 2  = 64.3% to within-cluster heterogeneity. We did not find that the three-level model provided a significantly better fit compared to a two-level model with level 3 heterogeneity constrained to zero (χ 2 1  = 1.99, p  = 0.16).

Subgroup analyses found differences by treatment type ( F  = 34.83, df:3,33, p  < 0.001) and service location ( F  = 35.58, df:2,34, p  < 0.001). Meta regressions found low intensity ( β  = − 0.94, 95%CI: − 1.17, − 0.71, p  <  0.0 01), specialist treatment ( β  = − 2.06, 95%CI: − 2.81, − 1.32, p  <  0.0 01) and on campus locations were associated with lower proportions ( β  = − 0.93, 95%CI: − 1.15, − 0.71, p  < 0.001) (see Additional file 1 : Appendix S15, and Appendix S17 for sensitivity analyses).

Main findings

This is the first systematic review and meta-analysis to synthesize evidence relating to the proportion of university students using mental health services, and how this varies by service type. In summary, we found there are wide variety of services available taking varying proportions of students, although overwhelmingly these were from HICs, in particular the USA. Across studies when estimates were grouped and pooled in service categories, we found around a 1/3 of students use services overall while attending university, with around 1/5 of students using outpatient services, and between 1 and 3% have used services that could be classed as residential. Our findings suggest where there is greater availability of support there is greater use, as indicated by higher use being associated with services offering a range of treatments. There was limited evidence to suggest services on campus were used more than those off campus, and students with more severe current psychological distress were associated with greater past service use. However, there are significant limitations with the current literature, including few international studies, particularly from LMICs, little clarity on how services link together, no studies of patient flow and limited consistent description of services.

Findings in the context of existing evidence

The finding of the proportion of students using mental health services is broadly consistent with average proportions of students reporting problems in previous literature from the USA and North America. In 2012 around 18% of students reported receiving any form of mental health treatment, and 36% among students with a likely mental health problem [ 20 ]. Annual cross-sectional surveys confirm that service use is aligned with prevalence in the USA and Canada with increases in service utilisation between 2007 and 2017 to around one third of university students using services [ 8 , 9 ]. Comparisons with estimates in non-student populations are difficult to interpret because of heterogeneous measures used to estimate need, limited international longitudinal analyses, and few studies assessing the effect of university on mental health trajectories [ 4 ]. A systematic review of service use among non-student young adults found only 16% reported using any mental health service, lower than our findings [ 81 ]. This is unlikely to be due to differences in need as individual studies suggest mental disorder has increased in both groups, at a similar rate [ 10 , 11 ]. US studies featured predominantly in both this previous review and ours, therefore differences in reported service use may reflect differences in the availability of services and insurance coverage between groups in the USA. Studies in non-students included relatively young populations with an average age of 21 [ 81 ]. In the USA context, the transition to university could prompt the earlier emergence of mental health difficulties as students may face significant new pressures, a new social context and new financial challenges prompting earlier help seeking [ 4 , 9 , 20 , 25 , 27 , 82 ].

Our review predominantly reports on studies of US university students in four-year institutions, and therefore our findings likely confounded by what is available there. Higher proportions of students using campus services maybe due to student’s awareness of, and ability to reach and pay for these services in comparison to other services [ 83 ]. Four-year US institutions receive comparably higher levels of funding than US community colleges, influencing their ability to provide students with comprehensive mental health services [ 23 , 47 , 84 ]. Studies using both national and regional US samples found four-year university students report higher use of services on campus compared to community college students, despite higher prevalence of mental health problems in community colleges [ 23 , 47 ]. Cost was cited as the most common barrier to seeking help among community college students [ 23 ]. International studies included in this review reported different patterns of service use, which may reflect different patterns of service provision, demand among students, and barriers to help seeking [ 73 , 74 , 75 , 78 , 79 , 80 ]. For example, countries such as Australia where there may be fewer barriers to support outside of university, students sought help from a broad range of providers, most frequent being General Practitioners [ 73 ]. The limited number of studies outside the USA may reflect the relatively recent increases in the number and diversity of students attending university in other HIC countries, such as the UK [ 4 ]. Only recent research has highlighted the very limited research focus on LMIC [ 85 ], perhaps the reflecting the potentially smaller proportion of their national populations attending university compared to most HICs [ 1 ]. However, recent efforts through the WHO WMH-ICS indicates some change in this field [ 6 , 16 ]. This in the context of the growing emphasis on the importance of global mental health and the role higher education might play in contributing to improvements in population health [ 1 , 3 ].

The level of heterogeneity observed was striking when compared to the published literature potentially illustrating the wide range of services, likely with a range of entry requirements, and populations of students. This could also reflect inequalities in population coverage and use of mental health services relative to need across the student populations, as noted in other literature [ 18 , 21 , 22 ]. A review in non-student populations found being female, Caucasian, homosexual, or bisexual meant you were more likely to use services, which is similar to findings in students [ 81 ]. However, in our review, some studies of international students had comparably lower use of services, one study reporting only 2.6% used a service [ 52 ]. Other studies examining use in other populations in our review reported much higher proportions, as high as 75% [ 73 ]. It may be that variation among students is even greater than non-students due to the wide variety of needs among students. Despite students in the USA and other HICs potentially having more available services, such as those on campus, these may be particularly underutilised by some groups who experience more significant barriers to help-seeking both inside and outside university [ 18 , 21 , 22 ]. If some groups of students are consistently underrepresented in services, it is unlikely activities and interventions these services provide will be appropriate for their needs, and will continue to be underutilised by these students [ 86 ].

Strengths and limitations

This is the first systematic review to summarise and pool evidence quantitatively about the management of student mental health. This allowed us to explore and then quantify variation in the way mental health services are used by university students. However, there are limitations to the current review. Firstly, generalising the findings of this review outside of the USA should be cautioned given the limited number of international studies. Secondly, there were specific challenges to classifying services studies described or listed. For example, it was not always clear whether the services were interpreted in the same way by all participants or services with similar names were comparable to each other between studies. While we double coded a random sample of these services, this could have introduced classification bias when grouping the services in this review. We found some outlying estimates that may have been explained by the broad definitions used. For example, ‘counselling’ could provide help for a range of needs or be interpreted differently by students answering a survey. While other reviews have commented that there is variation by treatment received, service location, and by specific populations of students [ 20 , 31 ]. There was not always detailed and consistent data across our included studies to thoroughly evaluate these relationships quantitatively. However, we used a range of synthesis methods to understand the literature.

The methods to examine use of mental health services in the included studies were heterogeneous. While most included binary response options, the reporting periods varied. This meant there were challenges determining whether students used a service at university or before they were students and whether students continued to use services from before university or were new presentations. This may have led to an overestimation of the proportion of students using mental health services. However, we did conduct sensitivity analyses where we excluded these estimates and used meta-regressions to control for reporting period in all analyses. Most of the studies were in the USA. We would therefore caution generalising the findings of this review beyond the USA given the specificities of the healthcare system and infrastructure available to students there, in contrast even to other Western countries.

Implications for practice, policy, and research

The findings from this review emphasise the importance of a range of service provision being available to students who are experiencing psychological distress, and supports current policy efforts to develop well integrated services to help span levels of need. However, reviews in countries with a significant policy emphasis on integration, such as the UK, highlight the challenges defining this process, and the traditionally top-down approach has led to mixed success [ 87 ]. The authors argue this may relate to the highly contextual nature of the problems integration aims to address, therefore it should focus on what needs to be done rather than simply the goal of integration [ 87 ]. The findings of our review, particularly the variety of services, groups of students and numbers using mental health services, support this point. This emphasises the need for detailed local needs assessments, the co-production of the process of integration with relevant stakeholders, and adaptations to meet the needs of the local student population [ 32 , 87 ].

Given the important developmental period students often attend university and the potential important role university’s could play in improving population mental health, the findings of the review suggest a series of important avenues for future research. (1) There is a urgent need to conduct robust international studies to understand student mental health need; (2) international research describing service models available to, acceptable to, and used by, students and similar aged young people; (3) given the few students using formal mental health services across all studies identified in this review, international research should continue to understand alternative models and interventions which might be acceptable and accessible students, such as task shifting, the use of technology, and capacity building within social networks [ 3 , 32 ]; (4) there are no studies of patient flow and how services are linked together which should be a priority of research particularly given the policy emphasis on integration; (5) there is a limited number of studies examining the adequacy of treatment students receive which could help understand how well services are meeting the needs of students who reach services [ 42 ]. (6) To understand how best to adapt current care pathways the experiences of students, healthcare professionals and other stakeholders need to be explored. In some HICs qualitative studies have spoken to students, and staff in counselling services [ 19 , 24 , 25 , 82 ], however given the variation of services we found in this review our findings emphasize the need to speak to healthcare professionals, students and other young in a range of settings; (7) The observed differences between the findings of this review and a review in non-student populations [ 81 ], it is crucial to understand whether university attendance adds additional risk to mental health trajectories. Our findings suggest significant inequalities in access to mental health services among students and settings, the literature should be systematically reviewed to examine this further.

Globally, future research should pay close attention to health and social inequalities between those with and without a university degree. In many countries, particularly those with a small proportions of people ultimately attaining a university degree, there is the potential to exacerbate inequalities by improving the health of a potentially privileged group of people [ 1 , 88 ]. Any initiatives aiming to address student mental health should be considered in the relation to wider population as part of a broader strategy to improve population mental health [ 3 ].

This review is the first effort to systematically describe mental health services available to students and quantify students’ use of them. Most studies were in HICs, in particularly the USA, where we found around a third of students had used a mental health service, similar to the proportion of students with symptoms indicative of mental disorder. However, we found significant variation in the utilisation of mental health services across populations of students, settings, and countries. There were some services, such as those on-campus, used more than others potentially reflecting supply and demand patterns in the included study settings. The empirical literature to date is very limited in terms of the relatively small number of international studies, and few studies examining how services link together, and how students move between them which limits our understanding of the problems students face. Our findings support the current renewed effort to study student mental health internationally and emphasises the importance of well-integrated services to support students’ needs.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Other materials are available in Additional file 1 : Appendices 1–17.

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Acknowledgements

Professor Steve Pilling, Dr Laura Gibbon and Dr Emma Broglia for their advice on the design and conduct of this review.

This report is independent research funded by the National Institute for Health Research ARC North Thames. The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care.

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Mr Tom Osborn (TO) directed the review; conducted the screening, data extraction and quality appraisal; and carried out coding and analyses. Ms. Siying Li (SL) conducted the screening, data extraction, quality appraisal and coding. Professor Peter Fonagy (PF) and Dr. Rob Saunders (RS) contributed to the planning of the review, advised throughout the review process, and commented on the draft. All authors read and approved the final manuscript.

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Additional file 1..

Appendix S1. PRISMA checklist. Appendix S2. Key words and MeSH terms. Appendix S3. Screening tool and eligibility assessment tool. Appendix S4. Data Extraction Form (2). Appendix S5. Relevant Sections from the eDESDE-LTC coding framework used for coding services. Appendix S6. search results. Appendix S7. Quality Appaisal (2). Appendix S8. Overall service use. Appendix S9. Overall outpatient service use. Appendix S10. Overall residential service use. Appendix S11. Sensitivity analyses. Appendix S12. Sensitivity analyses – overall service use. Appendix S13. Sensitivity analyses – overall outpatient service use. Appendix S14. Specific service use (multiple DESDE categories) analyses. Appendix S15. Specific outpatient service use analyses. Appendix S16. Sensitivity analyses - specific service use (multiple DESDE categories). Appendix S17. Sensitivity analyses - specific outpatient service use.

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Osborn, T.G., Li, S., Saunders, R. et al. University students’ use of mental health services: a systematic review and meta-analysis. Int J Ment Health Syst 16 , 57 (2022). https://doi.org/10.1186/s13033-022-00569-0

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Factors that influence mental health of university and college students in the UK: a systematic review

• Fiona Campbell 1 ,
• Lindsay Blank 1 ,
• Anna Cantrell 1 ,
• Susan Baxter 1 ,
• Christopher Blackmore 1 ,
• Jan Dixon 1 &
• Elizabeth Goyder 1  

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Worsening mental health of students in higher education is a public policy concern and the impact of measures to reduce transmission of COVID-19 has heightened awareness of this issue. Preventing poor mental health and supporting positive mental wellbeing needs to be based on an evidence informed understanding what factors influence the mental health of students.

To identify factors associated with mental health of students in higher education.

We undertook a systematic review of observational studies that measured factors associated with student mental wellbeing and poor mental health. Extensive searches were undertaken across five databases. We included studies undertaken in the UK and published within the last decade (2010–2020). Due to heterogeneity of factors, and diversity of outcomes used to measure wellbeing and poor mental health the findings were analysed and described narratively.

We included 31 studies, most of which were cross sectional in design. Those factors most strongly and consistently associated with increased risk of developing poor mental health included students with experiences of trauma in childhood, those that identify as LGBTQ and students with autism. Factors that promote wellbeing include developing strong and supportive social networks. Students who are prepared and able to adjust to the changes that moving into higher education presents also experience better mental health. Some behaviours that are associated with poor mental health include lack of engagement both with learning and leisure activities and poor mental health literacy.

Improved knowledge of factors associated with poor mental health and also those that increase mental wellbeing can provide a foundation for designing strategies and specific interventions that can prevent poor mental health and ensuring targeted support is available for students at increased risk.

Peer Review reports

Poor mental health of students in further and higher education is an increasing concern for public health and policy [ 1 , 2 , 3 , 4 ]. A 2020 Insight Network survey of students from 10 universities suggests that “1 in 5 students has a current mental health diagnosis” and that “almost half have experienced a serious psychological issue for which they felt they needed professional help”—an increase from 1 in 3 in the same survey conducted in 2018 [ 5 ]. A review of 105 Further Education (FE) colleges in England found that over a three-year period, 85% of colleges reported an increase in mental health difficulties [ 1 ]. Depression and anxiety were both prevalent and widespread in students; all colleges reported students experiencing depression and 99% reported students experiencing severe anxiety [ 5 , 6 ]. A UK cohort study found that levels of psychological distress increase on entering university [ 7 ], and recent evidence suggests that the prevalence of mental health problems among university students, including self-harm and suicide, is rising, [ 3 , 4 ] with increases in demand for services to support student mental health and reports of some universities finding a doubling of the number of students accessing support [ 8 ]. These common mental health difficulties clearly present considerable threat to the mental health and wellbeing of students but their impact also has educational, social and economic consequences such as academic underperformance and increased risk of dropping out of university [ 9 , 10 ].

Policy changes may have had an influence on the student experience, and on the levels of mental health problems seen in the student population; the biggest change has arguably been the move to widen higher education participation and to enable a more diverse demographic to access University education. The trend for widening participation has been continually rising since the late 1960s [ 11 ] but gained impetus in the 2000s through the work of the Higher Education Funding Council for England (HEFCE). Macaskill (2013) [ 12 ] suggests that the increased access to higher education will have resulted in more students attending university from minority groups and less affluent backgrounds, meaning that more students may be vulnerable to mental health problems, and these students may also experience greater challenges in making the transition to higher education.

Another significant change has been the introduction of tuition fees in 1998, which required students to self fund up to £1,000 per academic year. Since then, tuition fees have increased significantly for many students. With the abolition of maintenance grants, around 96% of government support for students now comes in the form of student loans [ 13 ]. It is estimated that in 2017, UK students were graduating with average debts of £50,000, and this figure was even higher for the poorest students [ 13 ]. There is a clear association between a student’s mental health and financial well-being [ 14 ], with “increased financial concern being consistently associated with worse health” [ 15 ].

The extent to which the increase in poor mental health is also being seen amongst non-students of a similar age is not well understood and warrants further study. However, the increase in poor mental health specifically within students in higher education highlights a need to understand what the risk factors are and what might be done within these settings to ensure young people are learning and developing and transitioning into adulthood in environments that promote mental wellbeing.

Commencing higher education represents a key transition point in a young person’s life. It is a stage often accompanied by significant change combined with high expectations of high expectations from students of what university life will be like, and also high expectations from themselves and others around their own academic performance. Relevant factors include moving away from home, learning to live independently, developing new social networks, adjusting to new ways of learning, and now also dealing with the additional greater financial burdens that students now face.

The recent global COVID-19 pandemic has had considerable impact on mental health across society, and there is concern that younger people (ages 18–25) have been particularly affected. Data from Canada [ 16 ] indicate that among survey respondents, “almost two-thirds (64%) of those aged 15 to 24 reported a negative impact on their mental health, while just over one-third (35%) of those aged 65 and older reported a negative impact on their mental health since physical distancing began” (ibid, p.4). This suggests that older adults are more prepared for the kind of social isolation which has been brought about through the response to COVID-19, whereas young adults have found this more difficult to cope with. UK data from the National Union of Students reports that for over half of UK students, their mental health is worse than before the pandemic [ 17 ]. Before COVID-19, students were already reporting increasing levels of mental health problems [ 2 ], but the COVID-19 pandemic has added a layer of “chronic and unpredictable” stress, creating the perfect conditions for a mental health crisis [ 18 ]. An example of this is the referrals (both urgent and routine) of young people with eating disorders for treatment in the NHS which almost doubled in number from 2019 to 2020 [ 19 ]. The travel restrictions enforced during the pandemic have also impacted on student mental health, particularly for international students who may have been unable to commence studies or go home to see friends and family during holidays [ 20 ].

With the increasing awareness and concern in the higher education sector and national bodies regarding student mental health has come increasing focus on how to respond. Various guidelines and best practice have been developed, e.g. ‘Degrees of Disturbance’ [ 21 ], ‘Good Practice Guide on Responding to Student Mental Health Issues: Duty of Care Responsibilities for Student Services in Higher Education’ [ 22 ] and the recent ‘The University Mental Health Charter’ [ 2 ]. Universities UK produced a Good Practice Guide in 2015 called “Student mental wellbeing in higher education” [ 23 ]. An increasing number of initiatives have emerged that are either student-led or jointly developed with students, and which reflect the increasing emphasis students and student bodies place on mental health and well-being and the increased demand for mental health support: Examples include: Nightline— www.nightline.ac.uk , Students Against Depression— www.studentsagainstdepression.org , Student Minds— www.studentminds.org.uk/student-minds-and-mental-wealth.html and The Alliance for Student-Led Wellbeing— www.alliancestudentwellbeing.weebly.com/ .

Although requests for professional support have increased substantially [ 24 ] only a third of students with mental health problems seek support from counselling services in the UK [ 12 ]. Many students encounter barriers to seeking help such as stigma or lack of awareness of services [ 25 ], and without formal support or intervention, there is a risk of deterioration. FE colleges and universities have identified the need to move beyond traditional forms of support and provide alternative, more accessible interventions aimed at improving mental health and well-being. Higher education institutions have a unique opportunity to identify, prevent, and treat mental health problems because they provide support in multiple aspects of students’ lives including academic studies, recreational activities, pastoral and counselling services, and residential accommodation.

In order to develop services that better meet the needs of students and design environments that are supportive of developing mental wellbeing it is necessary to explore and better understand the factors that lead to poor mental health in students.

Research objectives

The overall aim of this review was to identify, appraise and synthesise existing research evidence that explores the aetiology of poor mental health and mental wellbeing amongst students in tertiary level education. We aimed to gain a better understanding of the mechanisms that lead to poor mental health amongst tertiary level students and, in so doing, make evidence-based recommendations for policy, practice and future research priorities. Specific objectives in line with the project brief were to:

To co-produce with stakeholders a conceptual framework for exploring the factors associated with poorer mental health in students in tertiary settings. The factors may be both predictive, identifying students at risk, or causal, explaining why they are at risk. They may also be protective, promoting mental wellbeing.

To conduct a review drawing on qualitative studies, observational studies and surveys to explore the aetiology of poor mental health in students in university and college settings and identify factors which promote mental wellbeing amongst students.

To identify evidence-based recommendations for policy, service provision and future research that focus on prevention and early identification of poor mental health

Methodology

Identification of relevant evidence.

The following inclusion criteria were used to guide the development of the search strategy and the selection of studies.

We included students from a variety of further education settings (16 yrs + or 18 yrs + , including mature students, international students, distance learning students, students at specific transition points).

Universities and colleges in the UK. We were also interested in the context prior to the beginning of tertiary education, including factors during transition from home and secondary education or existing employment to tertiary education.

Any factor shown to be associated with mental health of students in tertiary level education. This included clinical indicators such as diagnosis and treatment and/or referral for depression and anxiety. Self-reported measures of wellbeing, happiness, stress, anxiety and depression were included. We did not include measures of academic achievement or engagement with learning as indicators of mental wellbeing.

Study design

We included cross-sectional and longitudinal studies that looked at factors associated with mental health outcomes in Table 5 .

Data extraction and quality appraisal

We extracted and tabulated key data from the included papers. Data extraction was undertaken by one reviewer, with a 10% sample checked for accuracy and consistency The quality of the included studies were evaluated using the Newcastle-Ottawa Scale [ 26 ] and the findings of the quality appraisal used in weighting the strength of associations and also identifying gaps for future high quality research.

Involvement of stakeholders

We recruited students, ex-students and parents of students to a public involvement group which met on-line three times during the process of the review and following the completion of the review. During a workshop meeting we asked for members of the group to draw on their personal experiences to suggest factors which were not mentioned in the literature.

Methods of synthesis

We undertook a narrative synthesis [ 27 ] due to the heterogeneity in the exposures and outcomes that were measured across the studies. Data showing the direction of effects and the strength of the association (correlation coefficients) were recorded and tabulated to aid comparison between studies.

Search strategy

Searches were conducted in the following electronic databases: Medline, Applied Social Sciences Index and Abstracts (ASSIA), International Bibliography of Social Sciences (IBSS), Science,PsycINFO and Science and Social Sciences Ciatation Indexes. Additional searches of grey literature, and reference lists of included studies were also undertaken.

The search strategy combined a number of terms relating to students and mental health and risk factors. The search terms included both subject (MeSH) and free-text searches. The searches were limited to papers about humans in English, published from 2010 to June 2020. The flow of studies through the review process is summarised in Fig.  1 .

Flow diagram

The full search strategy for Medline is provided in Appendix 1 .

Thirty-one quantitative, observational studies (39 papers) met the inclusion criteria. The total number of students that participated in the quantitative studies was 17,476, with studies ranging in size from 57 to 3706. Eighteen studies recruited student participants from only one university; five studies (10 publications) [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ] included seven or more universities. Six studies (7 publications) [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ] only recruited first year students, while the majority of studies recruited students from a range of year groups. Five studies [ 39 , 42 , 43 , 44 , 45 ] recruited only, or mainly, psychology students which may impact on the generalisability of findings. A number of studies focused on students studying particular subjects including: nursing [ 46 ] medicine [ 47 ], business [ 48 ], sports science [ 49 ]. One study [ 50 ] recruited LGBTQ (lesbian, gay, bisexual, transgender, intersex, queer/questioning) students, and one [ 51 ] recruited students who had attended hospital having self-harmed. In 27 of the studies, there were more female than male participants. The mean age of the participants ranged from 19 to 28 years. Ethnicity was not reported in 19 of the studies. Where ethnicity was reported, the proportion that were ‘white British’ ranged from 71 – 90%. See Table 1 for a summary of the characteristics of the included studies and the participants.

Design and quality appraisal of the included studies

The majority of included studies ( n  = 22) were cross-sectional surveys. Nine studies (10 publications) [ 35 , 36 , 39 , 41 , 43 , 50 , 51 , 52 , 53 , 62 ] were longitudinal in design, recording survey data at different time points to explore changes in the variables being measured. The duration of time that these studies covered ranged from 19 weeks to 12 years. Most of the studies ( n  = 22) only recruited participants from a single university. The use of one university setting and the large number of studies that recruited only psychology students weakens the wider applicability of the included studies.

Quantitative variables

Included studies ( n  = 31) measured a wide range of variables and explored their association with poor mental health and wellbeing. These included individual level factors: age, gender, sexual orientation, ethnicity and a range of psychological variables. They also included factors that related to mental health variables (family history, personal history and mental health literacy), pre-university factors (childhood trauma and parenting behaviour. University level factors including social isolation, adjustment and engagement with learning. Their association was measured against different measures of positive mental health and poor mental health.

Measurement of association and the strength of that association has some limitations in addressing our research question. It cannot prove causality, and nor can it capture fully the complexity of the inter-relationship and compounding aspect of the variables. For example, the stress of adjustment may be manageable, until it is combined with feeling isolated and out of place. Measurement itself may also be misleading, only capturing what is measureable, and may miss variables that are important but not known. We included both qualitative and PPI input to identify missed but important variables.

The wide range of variables and different outcomes, with few studies measuring the same variable and outcomes, prevented meta-analyses of findings which are therefore described narratively.

The variables described were categorised during the analyses into the following categories:

Vulnerabilities – factors that are associated with poor mental health

Individual level factors including; age, ethnicity, gender and a range of psychological variables were all measured against different mental health outcomes including depression, anxiety, paranoia, and suicidal behaviour, self-harm, coping and emotional intelligence.

Six studies [ 40 , 42 , 47 , 50 , 60 , 63 ] examined a student’s ages and association with mental health. There was inconsistency in the study findings, with studies finding that age (21 or older) was associated with fewer depressive symptoms, lower likelihood of suicide ideation and attempt, self-harm, and positively associated with better coping skills and mental wellbeing. This finding was not however consistent across studies and the association was weak. Theoretical models that seek to explain this mechanism have suggested that older age groups may cope better due to emotion-regulation strategies improving with age [ 67 ]. However, those over 30 experienced greater financial stress than those aged 17-19 in another study [ 63 ].

Sexual orientation

Four studies [ 33 , 40 , 64 , 68 ] examined the association between poor mental health and sexual orientation status. In all of the studies LGBTQ students were at significantly greater risk of mental health problems including depression [ 40 ], anxiety [ 40 ], suicidal behaviour [ 33 , 40 , 64 ], self harm [ 33 , 40 , 64 ], use of mental health services [ 33 ] and low levels of wellbeing [ 68 ]. The risk of mental health problems in these students compared with heterosexual students, ranged from OR 1.4 to 4.5. This elevated risk may reflect the greater levels of isolation and discrimination commonly experienced by minority groups.

Nine studies [ 33 , 38 , 39 , 40 , 42 , 47 , 50 , 60 , 63 ] examined whether gender was associated mental health variables. Two studies [ 33 , 47 ] found that being female was statistically significantly associated with use of mental health services, having a current mental health problem, suicide risk, self harm [ 33 ] and depression [ 47 ]. The results were not consistent, with another study [ 60 ] finding the association was not significant. Three studies [ 39 , 40 , 42 ] that considered mediating variables such as adaptability and coping found no difference or very weak associations.

Two studies [ 47 , 60 ] examined the extent to which ethnicity was associated with mental health One study [ 47 ] reported that the risks of depression were significantly greater for those who categorised themselves as non-white (OR 8.36 p = 0.004). Non-white ethnicity was also associated with poorer mental health in another cross-sectional study [ 63 ]. There was no significant difference in the McIntyre et al. (2018) study [ 60 ]. The small number of participants from ethnic minority groups represented across the studies means that this data is very limited.

Family factors

Six studies [ 33 , 40 , 42 , 50 , 60 ] explored the association of a concept that related to a student’s experiences in childhood and before going to university. Three studies [ 40 , 50 , 60 ] explored the impact of ACEs (Adverse Childhood Experiences) assessed using the same scale by Feletti (2009) [ 69 ] and another explored the impact of abuse in childhood [ 46 ]. Two studies examined the impact of attachment anxiety and avoidance [ 42 ], and parental acceptance [ 46 , 59 ]. The studies measured different mental health outcomes including; positive and negative affect, coping, suicide risk, suicide attempt, current mental health problem, use of mental health services, psychological adjustment, depression and anxiety.

The three studies that explored the impact of ACE’s all found a significant and positive relationship with poor mental health amongst university students. O’Neill et al. (2018) [ 50 ] in a longitudinal study ( n  = 739) showed that there was in increased likelihood in self-harm and suicidal behaviours in those with either moderate or high levels of childhood adversities (OR:5.5 to 8.6) [ 50 ]. McIntyre et al. (2018) [ 60 ] ( n  = 1135) also explored other dimensions of adversity including childhood trauma through multiple regression analysis with other predictive variables. They found that childhood trauma was significantly positively correlated with anxiety, depression and paranoia (ß = 0.18, 0.09, 0.18) though the association was not as strong as the correlation seen for loneliness (ß = 0.40) [ 60 ]. McLafferty et al. (2019) [ 40 ] explored the compounding impact of childhood adversity and negative parenting practices (over-control, overprotection and overindulgence) on poor mental health (depression OR 1.8, anxiety OR 2.1 suicidal behaviour OR 2.3, self-harm OR 2.0).

Gaan et al.’s (2019) survey of LGBTQ students ( n  = 1567) found in a multivariate analyses that sexual abuse, other abuse from violence from someone close, and being female had the highest odds ratios for poor mental health and were significantly associated with all poor mental health outcomes [ 33 ].

While childhood trauma and past abuse poses a risk to mental health for all young people it may place additional stresses for students at university. Entry to university represents life stage where there is potential exposure to new and additional stressors, and the possibility that these students may become more isolated and find it more difficult to develop a sense of belonging. Students may be separated for the first time from protective friendships. However, the mechanisms that link childhood adversities and negative psychopathology, self-harm and suicidal behaviour are not clear [ 40 ]. McLafferty et al. (2019) also measured the ability to cope and these are not always impacted by childhood adversities [ 40 ]. They suggest that some children learn to cope and build resilience that may be beneficial.

McLafferty et al. (2019) [ 40 ] also studied parenting practices. Parental over-control and over-indulgence was also related to significantly poorer coping (OR -0.075 p  < 0.05) and this was related to developing poorer coping scores (OR -0.21 p  < 0.001) [ 40 ]. These parenting factors only became risk factors when stress levels were high for students at university. It should be noted that these studies used self-report, and responses regarding views of parenting may be subjective and open to interpretation. Lloyd et al.’s (2014) survey found significant positive correlations between perceived parental acceptance and students’ psychological adjustment, with paternal acceptance being the stronger predictor of adjustment.

Autistic students may display social communication and interaction deficits that can have negative emotional impacts. This may be particularly true during young adulthood, a period of increased social demands and expectations. Two studies [ 56 ] found that those with autism had a low but statistically significant association with poor social problem-solving skills and depression.

Mental health history

Three studies [ 47 , 51 , 68 ] investigated mental health variables and their impact on mental health of students in higher education. These included; a family history of mental illness and a personal history of mental illness.

Students with a family history or a personal history of mental illness appear to have a significantly greater risk of developing problems with mental health at university [ 47 ]. Mahadevan et al. (2010) [ 51 ] found that university students who self-harm have a significantly greater risk (OR 5.33) of having an eating disorder than a comparison group of young adults who self-harm but are not students.

Buffers – factors that are protective of mental wellbeing

Psychological factors.

Twelve studies [ 29 , 39 , 40 , 41 , 42 , 43 , 46 , 49 , 54 , 58 , 64 ] assessed the association of a range of psychological variables and different aspects of mental wellbeing and poor mental health. We categorised these into the following two categories: firstly, psychological variables measuring an individual’s response to change and stressors including adaptability, resilience, grit and emotional regulation [ 39 , 40 , 41 , 42 , 43 , 46 , 49 , 54 , 58 ] and secondly, those that measure self-esteem and body image [ 29 , 64 ].

The evidence from the eight included quantitative studies suggests that students with psychological strengths including; optimism, self-efficacy [ 70 ], resilience, grit [ 58 ], use of positive reappraisal [ 49 ], helpful coping strategies [ 42 ] and emotional intelligence [ 41 , 46 ] are more likely to experience greater mental wellbeing (see Table 2 for a description of the psychological variables measured). The positive association between these psychological strengths and mental well-being had a positive affect with associations ranging from r  = 0.2–0.5 and OR1.27 [ 41 , 43 , 46 , 49 , 54 ] (low to moderate strength of association). The negative associations with depressive symptoms are also statistically significant but with a weaker association ( r  = -0.2—0.3) [ 43 , 49 , 54 ].

Denovan (2017a) [ 43 ] in a longitudinal study found that the association between psychological strengths and positive mental wellbeing was not static and that not all the strengths remained statistically significant over time. The only factors that remained significant during the transition period were self-efficacy and optimism, remaining statistically significant as they started university and 6 months later.

Parental factors

Only one study [ 59 ] explored family factors associated with the development of psychological strengths that would equip young people as they managed the challenges and stressors encountered during the transition to higher education. Lloyd et al. (2014) [ 59 ] found that perceived maternal and paternal acceptance made significant and unique contributions to students’ psychological adjustment. Their research methods are limited by their reliance on retrospective measures and self-report measures of variables, and these results could be influenced by recall bias.

Two studies [ 29 , 64 ] considered the impact of how individuals view themselves on poor mental health. One study considered the impact of self-esteem and the association with non-accidental self-injury (NSSI) and suicide attempt amongst 734 university students. As rates of suicide and NSSI are higher amongst LGBT (lesbian, gay, bisexual, transgender) students, the prevalence of low self-esteem was compared. There was a low but statistically significant association between low self-esteem and NSSI, though not for suicide attempt. A large survey, including participants from seven universities [ 42 ] compared depressive symptoms in students with marked body image concerns, reporting that the risk of depressive symptoms was greater (OR 2.93) than for those with lower levels of body image concerns.

Mental health literacy and help seeking behaviour

Two studies [ 48 , 68 ] investigated attitudes to mental illness, mental health literacy and help seeking for mental health problems.

University students who lack sufficient mental health literacy skills to be able to recognise problems or where there are attitudes that foster shame at admitting to having mental health problems can result in students not recognising problems and/or failing to seek professional help [ 48 , 68 ]. Gorcyznski et al. (2017) [ 68 ] found that women and those who had a history of previous mental health problems exhibited significantly higher levels of mental health literacy. Greater mental health literacy was associated with an increased likelihood that individuals would seek help for mental health problems. They found that many students find it hard to identify symptoms of mental health problems and that 42% of students are unaware of where to access available resources. Of those who expressed an intention to seek help for mental health problems, most expressed a preference for online resources, and seeking help from family and friends, rather than medical professionals such as GPs.

Kotera et al. (2019) [ 48 ] identified self-compassion as an explanatory variable, reducing social comparison, promoting self-acceptance and recognition that discomfort is an inevitable human experience. The study found a strong, significant correlation between self-compassion and mental health symptoms ( r  = -0.6. p  < 0.01).

There again appears to be a cycle of reinforcement, where poor mental health symptoms are felt to be a source of shame and become hidden, help is not sought, and further isolation ensues, leading to further deterioration in mental health. Factors that can interrupt the cycle are self-compassion, leading to more readiness to seek help (see Fig.  2 ).

Poor mental health – cycles of reinforcement

Social networks

Nine studies [ 33 , 38 , 41 , 46 , 51 , 54 , 60 , 64 , 65 ] examined the concepts of loneliness and social support and its association with mental health in university students. One study also included students at other Higher Education Institutions [ 46 ]. Eight of the studies were surveys, and one was a retrospective case control study to examine the differences between university students and age-matched young people (non-university students) who attended hospital following deliberate self-harm [ 51 ].

Included studies demonstrated considerable variation in how they measured the concepts of social isolation, loneliness, social support and a sense of belonging. There were also differences in the types of outcomes measured to assess mental wellbeing and poor mental health. Grouping the studies within a broad category of ‘social factors’ therefore represents a limitation of this review given that different aspects of the phenomena may have been being measured. The tools used to measure these variables also differed. Only one scale (The UCLA loneliness scale) was used across multiple studies [ 41 , 60 , 65 ]. Diverse mental health outcomes were measured across the studies including positive affect, flourishing, self-harm, suicide risk, depression, anxiety and paranoia.

Three studies [ 41 , 60 , 62 ] measuring loneliness, two longitudinally [ 41 , 62 ], found a consistently positive association between loneliness and poor mental health in university students. Greater loneliness was linked to greater anxiety, stress, depression, poor general mental health, paranoia, alcohol abuse and eating disorder problems. The strength of the correlations ranged from 0–3-0.4 and were all statistically significant (see Tables 3 and 4 ). Loneliness was the strongest overall predictor of mental distress, of those measured. A strong identification with university friendship groups was most protective against distress relative to other social identities [ 60 ]. Whether poor mental health is the cause, or the result of loneliness was explored further in the studies. The results suggest that for general mental health, stress, depression and anxiety, loneliness induces or exacerbates symptoms of poor mental health over time [ 60 , 62 ]. The feedback cycle is evident, with loneliness leading to poor mental health which leads to withdrawal from social contacts and further exacerbation of loneliness.

Factors associated with protecting against loneliness by fostering supportive friendships and promoting mental wellbeing were also identified. Beliefs about the value of ‘leisure coping’, and attributes of resilience and emotional intelligence had a moderate, positive and significant association with developing mental wellbeing and were explored in three studies [ 46 , 54 , 66 ].

The transition to and first year at university represent critical times when friendships are developed. Thomas et al. (2020) [ 65 ] explored the factors that predict loneliness in the first year of university. A sense of community and higher levels of ‘social capital’ were significantly associated with lower levels of loneliness. ‘Social capital’ scales measure the development of emotionally supportive friendships and the ability to adjust to the disruption of old friendships as students transition to university. Students able to form close relationships within their first year at university are less likely to experience loneliness (r-0.09, r- 0.36, r- 0.34). One study [ 38 ] investigating the relationship between student experience and being the first in the family to attend university found that these students had lower ratings for peer group interactions.

Young adults at university and in higher education are facing multiple adjustments. Their ability to cope with these is influenced by many factors. Supportive friendships and a sense of belonging are factors that strengthen coping. Nightingale et al. (2012) undertook a longitudinal study to explore what factors were associated with university adjustment in a sample of first year students ( n  = 331) [ 41 ]. They found that higher skills of emotion management and emotional self-efficacy were predictive of stable adjustment. These students also reported the lowest levels of loneliness and depression. This group had the skills to recognise their emotions and cope with stressors and were confident to access support. Students with poor emotion management and low levels of emotional self-efficacy may benefit from intervention to support the development of adaptive coping strategies and seeking support.

The positive and negative feedback loops

The relationship between the variables described appeared to work in positive and negative feedback loops with high levels of social capital easing the formation of a social network which acts as a critical buffer to stressors (see Fig.  3 ). Social networks and support give further strengthening and reinforcement, stimulating positive affect, engagement and flourishing. These, in turn, widen and deepen social networks for support and enhance a sense of wellbeing. Conversely young people who enter the transition to university/higher education with less social capital are less likely to identify with and locate a social network; isolation may follow, along with loneliness, anxiety, further withdrawal from contact with social networks and learning, and depression.

Triggers – factors that may act in combination with other factors to lead to poor mental health

Stress is seen as playing a key role in the development of poor mental health for students in higher education. Theoretical models and empirical studies have suggested that increases in stress are associated with decreases in student mental health [ 12 , 43 ]. Students at university experience the well-recognised stressors associated with academic study such as exams and course work. However, perhaps less well recognised are the processes of transition, requiring adapting to a new social and academic environment (Fisher 1994 cited by Denovan 2017a) [ 43 ]. Por et al. (2011) [ 46 ] in a small ( n  = 130 prospective survey found a statistically significant correlation between higher levels of emotional intelligence and lower levels of perceived stress ( r  = 0.40). Higher perceived stress was also associated with negative affect in two studies [ 43 , 46 ], and strongly negatively associated with positive affect (correlation -0.62) [ 54 ].

University variables

Eleven studies [ 35 , 39 , 47 , 51 , 52 , 54 , 60 , 63 , 65 , 83 , 84 ] explored university variables, and their association with mental health outcomes. The range of factors and their impact on mental health variables is limited, and there is little overlap. Knowledge gaps are shown by factors highlighted by our PPI group as potentially important but not identified in the literature (see Table 5 ). It should be noted that these may reflect the focus of our review, and our exclusion of intervention studies which may evaluate university factors.

High levels of perceived stress caused by exam and course work pressure was positively associated with poor mental health and lack of wellbeing [ 51 , 52 , 54 ]. Other potential stressors including financial anxieties and accommodation factors appeared to be less consistently associated with mental health outcomes [ 35 , 38 , 47 , 51 , 60 , 62 ]. Important mediators and buffers to these stressors are coping strategies and supportive networks (see conceptual model Appendix 2 ). One impact of financial pressures was that students who worked longer hours had less interaction with their peers, limiting the opportunities for these students to benefit from the protective effects of social support.

Red flags – behaviours associated with poor mental health and/or wellbeing

Engagement with learning and leisure activities.

Engagement with learning activities was strongly and positively associated with characteristics of adaptability [ 39 ] and also happiness and wellbeing [ 52 ] (see Fig.  4 ). Boulton et al. (2019) [ 52 ] undertook a longitudinal survey of undergraduate students at a campus-based university. They found that engagement and wellbeing varied during the term but were strongly correlated.

Engagement and wellbeing

Engagement occurred in a wide range of activities and behaviours. The authors suggest that the strong correlation between all forms of engagement with learning has possible instrumental value for the design of systems to monitor student engagement. Monitoring engagement might be used to identify changes in the behaviour of individuals to assist tutors in providing support and pastoral care. Students also were found to benefit from good induction activities provided by the university. Greater induction satisfaction was positively and strongly associated with a sense of community at university and with lower levels of loneliness [ 65 ].

The inte r- related nature of these variables is depicted in Fig.  4 . Greater adaptability is strongly associated with more positive engagement in learning and university life. More engagement is associated with higher mental wellbeing.

Denovan et al. (2017b) [ 54 ] explored leisure coping, its psychosocial functions and its relationship with mental wellbeing. An individual’s beliefs about the benefits of leisure activities to manage stress, facilitate the development of companionship and enhance mood were positively associated with flourishing and were negatively associated with perceived stress. Resilience was also measured. Resilience was strongly and positively associated with leisure coping beliefs and with indicators of mental wellbeing. The authors conclude that resilient individuals are more likely to use constructive means of coping (such as leisure coping) to proactively cultivate positive emotions which counteract the experience of stress and promote wellbeing. Leisure coping is predictive of positive affect which provides a strategy to reduce stress and sustain coping. The belief that friendships acquired through leisure provide social support is an example of leisure coping belief. Strong emotionally attached friendships that develop through participation in shared leisure pursuits are predictive of higher levels of well-being. Friendship bonds formed with fellow students at university are particularly important for maintaining mental health, and opportunities need to be developed and supported to ensure that meaningful social connections are made.

The ‘broaden-and-build theory’ (Fredickson 2004 [ 85 ] cited by [ 54 ]) may offer an explanation for the association seen between resilience, leisure coping and psychological wellbeing. The theory is based upon the role that positive and negative emotions have in shaping human adaptation. Positive emotions broaden thinking, enabling the individual to consider a range of ways of dealing with and adapting to their environment. Conversely, negative emotions narrow thinking and limit options for adapting. The former facilitates flourishing, facilitating future wellbeing. Resilient individuals are more likely to use constructive means of coping which generate positive emotion (Tugade & Fredrickson 2004 [ 86 ], cited by [ 54 ]). Positive emotions therefore lead to growth in coping resources, leading to greater well-being.

Health behaviours at university

Seven studies [ 29 , 31 , 38 , 45 , 51 , 54 , 66 ] examined how lifestyle behaviours might be linked with mental health outcomes. The studies looked at leisure activities [ 63 , 80 ], diet [ 29 ], alcohol use [ 29 , 31 , 38 , 51 ] and sleep [ 45 ].

Depressive symptoms were independently associated with problem drinking and possible alcohol dependence for both genders but were not associated with frequency of drinking and heavy episodic drinking. Students with higher levels of depressive symptoms reported significantly more problem drinking and possible alcohol dependence [ 31 ]. Mahadevan et al. (2010) [ 51 ] compared students and non-students seen in hospital for self-harm and found no difference in harmful use of alcohol and illicit drugs.

Poor sleep quality and increased consumption of unhealthy foods were also positively associated with depressive symptoms and perceived stress [ 29 ]. The correlation with dietary behaviours and poor mental health outcomes was low, but also confirmed by the negative correlation between less perceived stress and depressive symptoms and consumption of a healthier diet.

Physical activity and participation in leisure pursuits were both strongly correlated with mental wellbeing ( r  = 0.4) [ 54 ], and negatively correlated with depressive symptoms and anxiety ( r  = -0.6, -0.7) [ 66 ].

Thirty studies measuring the association between a wide range of factors and poor mental health and mental wellbeing in university and college students were identified and included in this review. Our purpose was to identify the factors that contribute to the growing prevalence of poor mental health amongst students in tertiary level education within the UK. We also aimed to identify factors that promote mental wellbeing and protect against deteriorating poor mental health.

Loneliness and social isolation were strongly associated with poor mental health and a sense of belonging and a strong support network were strongly associated with mental wellbeing and happiness. These associations were strongly positive in the eight studies that explored them and are consistent with other meta-analyses exploring the link between social support and mental health [ 87 ].

Another factor that appeared to be protective was older age when starting university. A wide range of personal traits and characteristics were also explored. Those associated with resilience, ability to adjust and better coping led to improved mental wellbeing. Better engagement appeared as an important mediator to potentially explain the relationship between these two variables. Engagement led to students being able to then tap into those features that are protective and promoting of mental wellbeing.

Other important risk factors for poor mental wellbeing that emerged were those students with existing or previous mental illness. Students on the autism spectrum and those with poor social problem-solving also were more likely to suffer from poor mental health. Negative self-image was also associated with poor mental health at university. Eating disorders were strongly associated with poor mental wellbeing and were found to be far more of a risk in students at university than in a comparative group of young people not in higher education. Other studies of university students also found that pre-existing poor mental health was a strong predictor of poor mental health in university students [ 88 ].

At a family level, the experience of childhood trauma and adverse experiences including, for example, neglect, household dysfunction or abuse, were strongly associated with poor mental health in young people at university. Students with a greater number of ‘adverse childhood experiences’ were at significantly greater risk of poor mental health than those students without experience of childhood trauma. This was also identified in a review of factors associated with depression and suicide related outcomes amongst university undergraduate students [ 88 ].

Our findings, in contrast to findings from other studies of university students, did not find that female gender associated with poor mental health and wellbeing, and it also found that being a mature student was protective of mental wellbeing.

Exam and course work pressure was associated with perceived stress and poor mental health. A lack of engagement with learning activities was also associated with poor mental health. A number of variables were not consistently shown to be associated with poor mental health including financial concerns and accommodation factors. Very little evidence related to university organisation or support structures was assessed in the evidence. One study found that a good induction programme had benefits for student mental wellbeing and may be a factor that enables students to become a part of a social network positive reinforcement cycle. Involvement in leisure activities was also found to be associated with improved coping strategies and better mental wellbeing. Students with poorer mental health tended to also eat in a less healthy manner, consume more harmful levels of alcohol, and experience poorer sleep.

This evidence review of the factors that influence mental health and wellbeing indicate areas where universities and higher education settings could develop and evaluate innovations in practice. These include:

Interventions before university to improve preparation of young people and their families for the transition to university.

Exploratory work to identify the acceptability and feasibility of identifying students at risk or who many be exhibiting indications of deteriorating mental health

Interventions that set out to foster a sense of belonging and identify

Creating environments that are helpful for building social networks

Improving mental health literacy and access to high quality support services

This review has a number of limitations. Most of the included studies were cross-sectional in design, with a small number being longitudinal ( n  = 7), following students over a period of time to observe changes in the outcomes being measured. Two limitations of these sources of data is that they help to understand associations but do not reveal causality; secondly, we can only report the findings for those variables that were measured, and we therefore have to support causation in assuming these are the only factors that are related to mental health.

Furthermore, our approach has segregated and categorised variables in order to better understand the extent to which they impact mental health. This approach does not sufficiently explore or reveal the extent to which variables may compound one another, for example, feeling the stress of new ways of learning may not be a factor that influences mental health until it is combined with a sense of loneliness, anxiety about financial debt and a lack of parental support. We have used our PPI group and the development of vignettes of their experiences to seek to illustrate the compounding nature of the variables identified.

We limited our inclusion criteria to studies undertaken in the UK and published within the last decade (2009–2020), again meaning we may have limited our inclusion of relevant data. We also undertook single data extraction of data which may increase the risk of error in our data.

Understanding factors that influence students’ mental health and wellbeing offers the potential to find ways to identify strategies that enhance the students’ abilities to cope with the challenges of higher education. This review revealed a wide range of variables and the mechanisms that may explain how they impact upon mental wellbeing and increase the risk of poor mental health amongst students. It also identified a need for interventions that are implemented before young people make the transition to higher education. We both identified young people who are particularly vulnerable and the factors that arise that exacerbate poor mental health. We highlight that a sense of belonging and supportive networks are important buffers and that there are indicators including lack of engagement that may enable early intervention to provide targeted and appropriate support.

Availability of data and materials

Further details of the study and the findings can be provided on request to the lead author ([email protected]).

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Acknowledgements

We acknowledge the input from our public advisory group which included current and former students, and family members of students who have struggled with their mental health. The group gave us their extremely valuable insights to assist our understanding of the evidence.

This project was supported by funding from the National Institute for Health Research as part of the NIHR Public Health Research  Programme (fuding reference 127659 Public Health Review Team). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

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All of the included authors designed the project methods and prepared a protocol. A.C. designed the search strategy. F.C, L.B and C.B screened the identified citations and undertook data extraction. S.B. led the PPI involvement. JD participated as a member of the PPI group. F.C and L.B undertook the analysis. F.C. and L.B wrote the main manuscript text. All authors reviewed the manuscript. F.C designed Figs. 2 , 3 and 4 . The author(s) read and approved the final manuscript.

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Campbell, F., Blank, L., Cantrell, A. et al. Factors that influence mental health of university and college students in the UK: a systematic review. BMC Public Health 22 , 1778 (2022). https://doi.org/10.1186/s12889-022-13943-x

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• Volume 10, Issue 12
• Impact of the COVID-19 pandemic on mental health and well-being of communities: an exploratory qualitative study protocol
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• http://orcid.org/0000-0003-0180-0213 Anam Shahil Feroz 1 , 2 ,
• Naureen Akber Ali 3 ,
• Noshaba Akber Ali 1 ,
• Ridah Feroz 4 ,
• Salima Nazim Meghani 1 ,
• Sarah Saleem 1
• 1 Community Health Sciences , Aga Khan University , Karachi , Pakistan
• 2 Institute of Health Policy, Management and Evaluation , University of Toronto , Toronto , Ontario , Canada
• 3 School of Nursing and Midwifery , Aga Khan University , Karachi , Pakistan
• 4 Aga Khan University Institute for Educational Development , Karachi , Pakistan
• Correspondence to Ms Anam Shahil Feroz; anam.sahyl{at}gmail.com

Introduction The COVID-19 pandemic has certainly resulted in an increased level of anxiety and fear in communities in terms of disease management and infection spread. Due to fear and social stigma linked with COVID-19, many individuals in the community hide their disease and do not access healthcare facilities in a timely manner. In addition, with the widespread use of social media, rumours, myths and inaccurate information about the virus are spreading rapidly, leading to intensified irritability, fearfulness, insomnia, oppositional behaviours and somatic complaints. Considering the relevance of all these factors, we aim to explore the perceptions and attitudes of community members towards COVID-19 and its impact on their daily lives and mental well-being.

Methods and analysis This formative research will employ an exploratory qualitative research design using semistructured interviews and a purposive sampling approach. The data collection methods for this formative research will include indepth interviews with community members. The study will be conducted in the Karimabad Federal B Area and in the Garden (East and West) community settings in Karachi, Pakistan. The community members of these areas have been selected purposively for the interview. Study data will be analysed thematically using NVivo V.12 Plus software.

Ethics and dissemination Ethical approval for this study has been obtained from the Aga Khan University Ethical Review Committee (2020-4825-10599). The results of the study will be disseminated to the scientific community and to the research subjects participating in the study. The findings will help us explore the perceptions and attitudes of different community members towards the COVID-19 pandemic and its impact on their daily lives and mental well-being.

• mental health
• public health

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https://doi.org/10.1136/bmjopen-2020-041641

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Strengths and limitations of this study

The mental health impact of the COVID-19 pandemic is likely to last much longer than the physical health impact, and this study is positioned well to explore the perceptions and attitudes of community members towards the pandemic and its impact on their daily lives and mental well-being.

This study will guide the development of context-specific innovative mental health programmes to support communities in the future.

One limitation is that to minimise the risk of infection all study respondents will be interviewed online over Zoom and hence the authors will not have the opportunity to build rapport with the respondents or obtain non-verbal cues during interviews.

The COVID-19 pandemic has affected almost 180 countries since it was first detected in Wuhan, China in December 2019. 1 2 The COVID-19 outbreak has been declared a public health emergency of international concern by the WHO. 3 The WHO estimates the global mortality to be about 3.4% 4 ; however, death rates vary between countries and across age groups. 5 In Pakistan, a total of 10 880 cases and 228 deaths due to COVID-19 infection have been reported to date. 6

The worldwide COVID-19 pandemic has not only incurred massive challenges to the global supply chains and healthcare systems but also has a detrimental effect on the overall health of individuals. 7 The pandemic has led to lockdowns and has created destructive impact on the societies at large. Most company employees, including daily wage workers, have been prohibited from going to their workplaces or have been asked to work from home, which has caused job-related insecurities and financial crises in the communities. 8 Educational institutions and training centres have also been closed, which resulted in children losing their routine of going to schools, studying and socialising with their peers. Delay in examinations is likewise a huge stressor for students. 8 Alongside this, parents have been struggling with creating a structured milieu for their children. 9 COVID-19 has hindered the normal routine life of every individual, be it children, teenagers, adults or the elderly. The crisis is engendering burden throughout populations and communities, particularly in developing countries such as Pakistan which face major challenges due to fragile healthcare systems and poor economic structures. 10

The COVID-19 pandemic has certainly resulted in an increased level of anxiety and fear in communities in terms of disease management and infection spread. 8 Further, the highly contagious nature of COVID-19 has also escalated confusion, fear and panic among community residents. Moreover, social distancing is often an unpleasant experience for community members and for patients as it adds to mental suffering, particularly in the local setting where get-togethers with friends and families are a major source of entertainment. 9 Recent studies also showed that individuals who are following social distancing rules experience loneliness, causing a substantial level of distress in the form of anxiety, stress, anger, misperception and post-traumatic stress symptoms. 8 11 Separation from family members, loss of autonomy, insecurity over disease status, inadequate supplies, inadequate information, financial loss, frustration, stigma and boredom are all major stressors that can create drastic impact on an individual’s life. 11 Due to fear and social stigma linked with COVID-19, many individuals in the community hide their disease and do not access healthcare facilities in a timely manner. 12 With the widespread use of social media, 13 rumours, myths and inaccurate information about COVID-19 are also spreading rapidly, not only among adults but are also carried on to children, leading to intensified irritability, fearfulness, insomnia, oppositional behaviours and somatic complaints. 9 The psychological symptoms associated with COVID-19 at the community level are also manifested as anxiety-driven panic buying, resulting in exhaustion of resources from the market. 14 Some level of panic also dwells in the community due to the unavailability of essential protective equipment, particularly masks and sanitisers. 15 Similarly, mental health issues, including depression, anxiety, panic attacks, psychotic symptoms and even suicide, were reported during the early severe acute respiratory syndrome outbreak. 16 17 COVID-19 is likely posing a similar risk throughout the world. 12

The fear of transmitting the disease or a family member falling ill is a probable mental function of human nature, but at some point the psychological fear of the disease generates more anxiety than the disease itself. Therefore, mental health problems are likely to increase among community residents during an epidemic situation. Considering the relevance of all these factors, we aim to explore the perceptions and attitudes towards COVID-19 among community residents and the impact of these perceptions and attitude on their daily lives and mental well-being.

Methods and analysis

Study design.

This study will employ an exploratory qualitative research design using semistructured interviews and a purposive sampling approach. The data collection methods for this formative research will include indepth interviews (IDIs) with community members. The IDIs aim to explore perceptions of community members towards COVID-19 and its impact on their mental well-being.

Study setting and study participants

The study will be conducted in two communities in Karachi City: Karimabad Federal B Area Block 3 Gulberg Town, and Garden East and Garden West. Karimabad is a neighbourhood in the Karachi Central District of Karachi, Pakistan, situated in the south of Gulberg Town bordering Liaquatabad, Gharibabad and Federal B Area. The population of this neighbourhood is predominantly Ismailis. People living here belong mostly to the middle class to the lower middle class. It is also known for its wholesale market of sports goods and stationery. Garden is an upmarket neighbourhood in the Karachi South District of Karachi, Pakistan, subdivided into two neighbourhoods: Garden East and Garden West. It is the residential area around the Karachi Zoological Gardens; hence, it is popularly known as the ‘Garden’ area. The population of Garden used to be primarily Ismailis and Goan Catholics but has seen an increasing number of Memons, Pashtuns and Baloch. These areas have been selected purposively because the few members of these communities are already known to one of the coinvestigators. The coinvestigator will serve as a gatekeeper for providing entrance to the community for the purpose of this study. Adult community members of different ages and both genders will be interviewed from both sites, as mentioned in table 1 . Interview participants will be selected following the eligibility criteria.

• View inline

Study participants for indepth interviews

IDIs with community members

We will conduct IDIs with community members to explore the perceptions and attitudes of community members towards COVID-19 and its effects on their daily lives and mental well-being. IDI participants will be identified via the community WhatsApp group, and will be invited for an interview via a WhatsApp message or email. Consent will be taken over email or WhatsApp before the interview begins, where they will agree that the interview can be audio-recorded and that written notes can be taken. The interviews will be conducted either in Urdu or in English language, and each interview will last around 40–50 min. Study participants will be assured that their information will remain confidential and that no identifying features will be mentioned on the transcript. The major themes will include a general discussion about participants’ knowledge and perceptions about the COVID-19 pandemic, perceptions on safety measures, and perceived challenges in the current situation and its impact on their mental well-being. We anticipate that 24–30 interviews will be conducted, but we will cease interviews once data saturation has been achieved. Data saturation is the point when no new themes emerge from the additional interviews. Data collection will occur concurrently with data analysis to determine data saturation point. The audio recordings will be transcribed by a transcriptionist within 24 hours of the interviews.

An interview guide for IDIs is shown in online supplemental annex 1 .

Supplemental material

Eligibility criteria.

The following are the criteria for inclusion and exclusion of study participants:

Inclusion criteria

Residents of Garden (East and West) and Karimabad Federal B Area of Karachi who have not contracted the disease.

Exclusion criteria

Those who refuse to participate in the study.

Those who have experienced COVID-19 and are undergoing treatment.

Those who are suspected for COVID-19 and have been isolated/quarantined.

Family members of COVID-19-positive cases.

Data collection procedure

A semistructured interview guide has been developed for community members. The initial questions on the guide will help to explore participants’ perceptions and attitudes towards COVID-19. Additional questions on the guide will assess the impact of these perceptions and attitude on the daily lives and mental health and well-being of community residents. All semistructured interviews will be conducted online via Zoom or WhatsApp. Interviews will be scheduled at the participant’s convenient day and time. Interviews are anticipated to begin on 1 December 2020.

Patient and public involvement

No patients were involved.

Data analysis

We will transcribe and translate collected data into English language by listening to the audio recordings in order to conduct a thematic analysis. NVivo V.12 Plus software will be used to import, organise and explore data for analysis. Two independent researchers will read the transcripts at various times to develop familiarity and clarification with the data. We will employ an iterative process which will help us to label data and generate new categories to identify emergent themes. The recorded text will be divided into shortened units and labelled as a ‘code’ without losing the main essence of the research study. Subsequently, codes will be analysed and merged into comparable categories. Lastly, the same categories will be grouped into subthemes and final themes. To ensure inter-rater reliability, two independent investigators will perform the coding, category creation and thematic analyses. Discrepancies between the two investigators will be resolved through consensus meetings to reduce researcher bias.

Ethics and dissemination

Study participants will be asked to provide informed, written consent prior to participation in the study. The informed consent form can be submitted by the participant via WhatsApp or email. Participants who are unable to write their names will be asked to provide a thumbprint to symbolise their consent to participate. Ethical approval for this study has been obtained from the Aga Khan University Ethical Review Committee (2020-4825-10599). The study results will be disseminated to the scientific community and to the research subjects participating in the study. The findings will help us explore the perceptions and attitudes of different community members towards the COVID-19 pandemic and its impact on their daily lives and mental well-being.

The findings of this study will help us to explore the perceptions and attitudes towards the COVID-19 pandemic and its impact on the daily lives and mental well-being of individuals in the community. Besides, an indepth understanding of the needs of the community will be identified, which will help us develop context-specific innovative mental health programmes to support communities in the future. The study will provide insights into how communities are managing their lives under such a difficult situation.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

ASF and NAA are joint first authors.

Contributors ASF and NAA conceived the study. ASF, NAA, RF, NA, SNM and SS contributed to the development of the study design and final protocols for sample selection and interviews. ASF and NAA contributed to writing the manuscript. All authors reviewed and approved the final version of the paper.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Open Access

Peer-reviewed

Research Article

Mental health coping strategies and support needs among marginalised further and higher education students in the UK: A cross-sectional study

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Social Work and Wellbeing, Edge Hill University, Ormskirk, United Kingdom

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation School of Medicine and Dentistry, University of Central Lancashire, Preston, United Kingdom

Roles Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Roles Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing

Roles Formal analysis, Investigation, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing

Roles Conceptualization, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing

Roles Investigation, Methodology, Resources, Validation, Writing – original draft, Writing – review & editing

Affiliation Manchester Institute of Education, The University of Manchester, Manchester, United Kingdom

Roles Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Roles Conceptualization, Investigation, Methodology, Resources, Validation, Writing – original draft, Writing – review & editing

Roles Conceptualization, Formal analysis, Investigation, Methodology, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliations Department of Social Work and Wellbeing, Edge Hill University, Ormskirk, United Kingdom, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom

Roles Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

•  [ ... ],

Roles Conceptualization, Investigation, Methodology, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

• [ view all ]
• [ view less ]
• Shaun Liverpool, 
• Mohammed Moinuddin, 
• Katie Bracegirdle, 
• Jade Eddison, 
• Seyi Joseph, 
• Supritha Aithal, 
• Eve Allen, 
• Parise Carmichael-Murphy, 
• John Marsden, 

• Published: June 17, 2024
• https://doi.org/10.1371/journal.pmen.0000046
• Reader Comments

Students who are marginalised based on varying identities, backgrounds and characteristics are highly vulnerable to mental health challenges, but many do not receive appropriate support from healthcare services. Several barriers have been identified, including cultural and systemic factors. Therefore, everyday coping strategies and support in different settings are vital. This study examines the mental health coping strategies and support needs among marginalised students in the United Kingdom (UK). We analysed qualitative and quantitative data from a cross-sectional survey conducted between December 2021 and July 2022. Statistical analysis was conducted on data obtained using the abbreviated version of the Coping Orientation to Problems Experienced Inventory (Brief-COPE). Qualitative content analysis was applied to data collected using open-ended questions. From a subsample of 788 further and higher education students, 581 (73.7%) students (M = 25 years, SD = 8.19) were categorised as marginalised based on ethnicity, sex/gender, sexuality, religious beliefs, first language, birth country, age (i.e., mature students), and having special education needs/disabilities. Marginalised students had significantly higher scores for problem-focused, emotion-focused and avoidant coping strategies/practices compared to other students. Coping strategies included talking to friends and family, practising religion or spirituality, engaging in creative/innovative activities like hobbies, using entertainment as a distraction, waiting to see if things improve and isolating. Students expressed a need for improved or tailored services, additional academic support, and appropriate social support. These included contemporary approaches to support mental health, such as online provisions, regular mentor/personal tutor meetings, lowered academic pressures and opportunities for organised peer support. The findings from this study highlight significant and timely evidence on coping strategies and support needs among a wide range of marginalised student groups in the UK. This study provides important knowledge that is useful to inform personalised culturally appropriate mental health support that can be offered in education settings.

Citation: Liverpool S, Moinuddin M, Bracegirdle K, Eddison J, Joseph S, Aithal S, et al. (2024) Mental health coping strategies and support needs among marginalised further and higher education students in the UK: A cross-sectional study. PLOS Ment Health 1(1): e0000046. https://doi.org/10.1371/journal.pmen.0000046

Editor: Jinjin Lu, Xi'an Jiaotong-Liverpool University, CHINA

Received: February 17, 2024; Accepted: April 17, 2024; Published: June 17, 2024

Copyright: © 2024 Liverpool et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data used in this study will be made accessible via the link below: https://figshare.com/s/636a2876d13ae39f66f8 .

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Student mental health and wellbeing.

The mental health and wellbeing of further and higher education students is a growing public health concern in the United Kingdom (UK) [ 1 – 4 ]. Studies have shown that most mental health disorders develop by the age of 25 years [ 5 ], placing students in an at-risk group to experience mental health problems and their related impact. Academic, financial, and social stressors are common factors impacting students’ mental health while at college and university [ 6 – 9 ]. In addition, some students experience role conflicts while juggling employment and caring responsibilities alongside education [ 10 , 11 ]. With the increased demand for mental health services, UK universities are seeking to expand services and interventions in this area [ 12 ]. Therefore, more information is needed to understand the needs of different groups of students to ensure all students can benefit from the services offered. This is especially important for students who are typically disadvantaged or excluded based on sociodemographic characteristics like race/ethnicity, sex/gender and sexuality, from here on, referred to as marginalised students.

Students who are marginalised not only face the general stressors of student life [ 13 , 14 ], but they are also susceptible to marginal-specific factors. Typically, these factors can increase vulnerability to stressful events in education settings. Such factors include first-generation unfamiliarity with the UK education system [ 15 ] and other cultural and language barriers [ 16 , 17 ]. These instances can result in students having difficulty making friends and developing negative self-identities [ 10 , 11 , 14 , 18 ], thereby provoking issues with belongingness, social isolation and feelings of difference [ 19 ].

Other aggravating factors that contribute to vulnerability among marginalised students include developing feelings of being unqualified to be at college or university due to receiving overt discrimination, microaggressions and stereotyping [ 14 , 20 ]. Accordingly, marginalised students are seen as more susceptible to mental health issues, such as depression, anxiety and somatic problems [ 10 , 21 – 23 ]. However, the literature suggests that there are several barriers to these students seeking professional support for their mental health, including racism, inequality, stigma, waiting times and lack of knowledge of how to get support [ 15 , 24 ].

Accessing mental health support

Students who are marginalised often report barriers to accessing professional mental health services in healthcare settings. Although the incidence of diagnoses in marginalised groups can be high, access to treatment may not always guarantee that a service user’s needs will be met [ 25 ]. In recent years, there has been great emphasis on identifying and addressing cultural factors such as competence, attitudes, beliefs, and perceptions among service users and providers of mental health services [ 26 ]. However, cultural adaptation of a service, therapy or intervention alone may not ameliorate structural disadvantage. For marginalised students, navigating structural disadvantage in both education and mental health services makes access to health and education-related outcomes difficult [ 27 ]. Yet, there is often a focus on outlining how mental health services fail to meet the needs of those who access them, but less is known about what the unmet needs are.

Structural competency has been highlighted as an important facet of global health to address how societal and social injustices shape and sustain health inequities. This framework has been used for understanding how larger social and political structures shape health outcomes and influence the delivery of healthcare services [ 28 , 29 ]. Therefore, it is important to consider health interventions at the structural level to ensure that the systems of disparity and inequality are targeted [ 30 ]. In this context, a better focus on the nature of existing mental health services in education settings, such as the types of mental health interventions offered, who delivers them, and how, can offer insight into what structures sustain inequality in mental health.

Mental health coping strategies and practices

In the absence of appropriate support, students engage in a variety of coping mechanisms which are sometimes shaped by sociocultural characteristics like gender, age, prior experiences, geographical location, and social groups. Previous research highlighted that problem-solving and social support were the most widely employed coping mechanisms [ 31 ]. More specifically, some students commonly use positive coping strategies such as meditation, mindfulness, and physical activities, while other students turn to acceptance, planning, and seeking out emotional support as coping mechanisms [ 32 ]. In contrast, dysfunctional coping strategies among some college students include mental and behavioural disengagements, such as avoidance or procrastination, substance use, and social withdrawal or isolation [ 33 , 34 ]. Among marginalised groups seeking support within their communities, relying on cultural networks and religious institutions for emotional and social support appears to be common [ 35 ]. These groups also employ coping strategies that involve processing stressful events on their own and talking about these events to close friends and family [ 36 ].

For students from marginalised groups, the importance of an inclusive education environment with responsive support systems is key [ 37 , 38 ]. In colleges and universities, it is increasingly understood that students benefit from a compassionate environment in which their mental health needs are recognised and where they feel a sense of belonging, supported by empathically attuned university staff [ 39 ]. The theoretical underpinnings of this approach can be found in the humanistic psychological frameworks of Maslow [ 40 ] and Rogers [ 41 ]. Maslow argued that individuals are motivated by two drivers: the need for safety and the need for development, with the need for safety taking precedence [ 40 ]. This means that when students feel unsettled and anxious in the classroom, they are more likely to focus on managing this anxious state. Once emotional safety has been established, however, then learning can be accelerated, and students shift their attention outwards and more freely engage with the learning environment. Hence the need for psychological safety is particularly true for students from marginalised groups. This then implies a need for support that is tailored to meet the needs of all students, so they can access personalised culturally appropriate mental health care.

Aims and objectives of this study

Based on the above, we analysed both qualitative and quantitative data collected from marginalised further and higher education students in the UK to explore opportunities for new intervention development and improved service provision. The specific objectives for the quantitative analysis were to assess the types and prevalence of different coping strategies/practices adopted by marginalised students. The qualitative data analysis was conducted to gain further insights into the different coping strategies/practices and to explore the mental health support needs of marginalised students. Therefore, we sought to answer the following research questions:

• What strategies/practices are used by marginalised students to cope with stress?
• What kinds of support do students from marginalised groups need or want in educational settings to support their mental health and wellbeing?

Study design

We adopted a pragmatic epistemological position, underpinned by a convergent mixed methods approach, to help understand the mental health coping and support needs among marginalised further and higher education students in the UK [ 42 ]. This study was guided by the standards for conducting and reporting mixed methods research [ 43 , 44 ] and informed by The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations [ 45 ].

Data source and preparation

We conducted an international survey between December 2021 and July 2022 that aimed to examine the mental health and wellbeing of further and higher education students returning to face-to-face (in-person) learning after the COVID-19 pandemic restrictions were lifted [ 46 ]. Owing to the aims of the current study, a sample of that dataset was deemed appropriate, since several studies highlighted that the COVID-19 pandemic was a stressful period for students [ 46 – 49 ]. The total sample from the original dataset consisted of N = 1160 post-secondary students recruited internationally from a variety of education settings, including colleges and universities [ 46 ].

Data were included in the current analysis if the survey respondents were UK-based students ( Fig 1 ). The second inclusion criterion was the completion of any of the key demographic questions that represented age, ethnicity, sex/gender/sexuality, birth country, first language and having special education needs/disabilities. Based on the responses to the demographic questions, students were categorised as marginalised based on the United Nations’ definitions of any group of persons which constitutes less than half of the general population and whose members share common characteristics like ethnicity, sex/gender, sexuality, religion or language, or a combination of any of these [ 50 , 51 ]. Based on consultations with students, being a mature student and having special education needs were also two important groups that are marginalised in education settings. Further details of the marginalised categories are defined below.

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https://doi.org/10.1371/journal.pmen.0000046.g001

Data collection questions

Demographics..

Data from key demographic variables were extracted. Age was measured on a continuous scale using the participant’s date of birth. Ethnicity was captured using self-identified categories (White, Black, Asian, Mixed or Other). Sex and Gender expressions were captured using pronouns (He/Him, She/Her, They/Them or Other) and responding to the question, “Do you identify with the same sex you were assigned at birth?”. Sexuality was captured using self-identified categories (Heterosexual, Homosexual, Bisexual or Other). Religion was captured based on the most common categories in the original dataset (Christian, Hindu, Muslim, Other or no religion). To identify students with additional needs, the responses to whether students had any physical, mental or learning difficulties that meant they required additional support were used (Yes, No, Unsure). The level of study was captured using UK-based categories of education (e.g., Undergraduate is Level 4–6). First language was captured based on responses to the item “Is English your first language?” and birth country or immigrant status was captured based on responses to the item “Are you currently living in your birth country?”. Based on the available data and guidance from student consultations, students were categorised as marginalised based on the following criteria;

• Ethnicity–students who self-identified as belonging to a non-White ethnic group (e.g., Black, Asian, Mixed or Other)
• Sex/Gender expression–students who responded “no” to if they identify with the same sex assigned at birth and/or preferences for the pronouns “they/them” or “other”
• Sexuality—students who self-identified as belonging to a non- Heterosexual group (e.g., Homosexual, Bisexual or Other)
• Religious beliefs–students who identified with religious/spiritual beliefs that represented less than 50% of the general population (e.g., Hindu, Muslim, Other)
• Special education needs/disabilities–students who indicated they required additional support during their programme because of physical, mental or learning difficulties
• First language–students who responded “no” to “Is English your first language?”
• Country of birth–student who responded “no” to “Are you currently living in your birth country?”
• Age–mature students who were over 21 years of age at the beginning of their undergraduate studies or over 25 years of age at the beginning of their postgraduate studies [ 52 ]
• Intersectionality was defined as having multiple marginalised characteristics or belonging to more than one of the above categories [ 53 ].

Coping strategies/practices were assessed using the responses on the BriefCOPE, which is an abbreviated version of the Coping Orientation to Problems Experienced Inventory [ 54 ]. The BriefCOPE consisted of 28 items, and each item was rated on a 4-point Likert scale ranging from “I have not been doing this at all (score 1)” to “I have been doing this a lot (score 4)”. The scores corresponded to 14 dimensions, each reflecting the use of a coping strategy: active coping, planning, acceptance, denial, self-distraction, use of substance, use of emotional support, use of instrumental support, behavioural disengagement, venting, positive reframing, humour, religion, and self-blame. The BriefCope is a validated instrument with Cronbach’s alpha scores ranging from 0.50–0.90 [ 55 ]. We adopted the tri-categorisation for this scale that corresponded to problem-focused, emotion-focused, or avoidant coping styles [ 56 ].

Open-ended questions.

The data from two open-ended questions were used to obtain students’ descriptions of their individual coping styles and mental health support needs. These items were developed based on consultations with students as described in the original study [ 46 ]. Therefore, responses to the following questions were included;

• When you need additional support to improve your mental health and wellbeing, what do you do?
• What role, if any, should your place of education (e.g., college or university) play in supporting your mental health and wellbeing?

Data analysis

Quantitative data analysis..

Continuous variables were summarised using means and 95% confidence intervals and presented based on the number of marginal characteristics and the corresponding category of coping styles. Categorical variables were summarised using frequencies. Linear regression modelling was applied to each of the three categories of coping strategies/practices (i.e., problem-focused, emotion-focused and avoidant) to assess the difference in coping strategy scores based on the number of marginalised characteristics. Beta coefficients, their standard errors and p-values were calculated. A two-tailed p-value of <0.05 was considered statistically significant. Quantitative data were analysed using the R Software, version 4.3.1 and SPSS, version 29.

Qualitative data analysis.

The qualitative data were analysed using qualitative content analysis, which comprised of de-contextualization and re-contextualization of emerging concepts [ 57 , 58 ]. More specifically, we were guided by the following steps: selecting the unit of analysis, coding all the data, revising the coding rules (if necessary), creating and defining the categories, revising the category scheme, and constructing themes [ 59 ]. First, familiarisation with the data was done through re-reading the quotes. Second, three researchers (SL, KB and JE) met to generate the initial coding of the data. Third, the team met weekly for recoding and clustering of the codes into potential themes. Based on consensus within the team, themes were considered dominant if there were more than n = 15 example quotes within the dataset. Qualitative data analysis and data management were conducted using Microsoft Excel and NVivo.

Reflexivity, reliability, and rigour

During the study process, various strategies were adopted to maintain rigour and trustworthiness. The research team consisted of a diverse group of researchers with expertise in qualitative, quantitative and mixed-methods research, as well as extensive knowledge and practice in mental health and social work. Students with a particular interest in counselling and mental health programmes who were part of a research internship were included as co-researchers. Analysing both quantitative and qualitative data helped obtain different but complementary information that facilitated a deeper understanding of the mental health coping strategies and needs of marginalised students in the UK. As for the quantitative analysis, we used the BriefCOPE, which was found to be reliable and valid in previous studies [ 60 , 61 ]. The team also included a statistician (MM) to guide the analysis and interpretation of the quantitative data. As for the qualitative study, we collaborated with students (JE, KB, SJ, NAR) to facilitate member checking and triangulation. Agreements prior to the data analysis were achieved through debriefing and weekly meetings. We used reflexivity among the qualitative data analysis team (SL, KB, JE, MO) for transparency and consistency. We discussed our views, experiences, and positionality on the topic. Shared notes, double coding and checking for inter-rater coding accuracy were incorporated as part of the audit trail and therefore were used for communicating and discussing the various perspectives towards data interpretation. Intersectionality and integration were captured in the interpretation and reporting of the findings. We also provided self-identified labels associated with direct quotes to support the transferability of our findings.

Ethical considerations

The original study from which our dataset was identified was reviewed and ethically approved by the Research Ethics Committee of the Faculty of Health, Social Care and Medicine at Edge Hill University (ETH2021-0231). As this was an analysis of a subset of the anonymised data, and the participants provided consent for their data to be used for health and care research, no further ethical approvals were required [ 62 ].

Characteristics of the sample

Data from 788 UK-based further and higher education students were included in our analysis. The average age of the total sample was 25 years (SD = 8.19, Range 16–77 years). Most students studied at the undergraduate level (485 or 61.5%) and were enrolled in full-time education (631 or 80.1%). Of the 788 students, 581 (73.7%) belonged to at least one marginalised group based on sex/gender (45 or 5.71%), language (103 or 13.07%), ethnicity (196 or 24.87%), birth country (156 or 19.8%), religious beliefs (74 or 9.39%), sexuality (193 or 24.49%), age (i.e., mature student status, 187 or 23.73%), and special education needs/disabilities (226 or 28.68%). 45.27% (263 out of 581) of the sample had multiple marginalised characteristics ( Table 1 ).

https://doi.org/10.1371/journal.pmen.0000046.t001

Quantitative findings

Prevalence of coping strategies/practices..

Overall, significantly higher mean scores were observed for emotion-focused coping, followed by problem-focused and avoidant coping strategies ( Table 2 , Fig 2 ). Compared to students who were not categorised as marginalised (reference point 0), students who were marginalised based on one or more characteristic had significantly higher scores for problem-focused coping (M = 18(CI = 17.2,18.8) vs M = 20.1(CI = 19.5,20.7)), emotion-focused coping (M = 25.3(CI = 24.3,26.3) vs M = 28.2(CI = 27.4,29)) and avoidant coping (M = 14.7(CI = 14.1,15.3) vs M = 16.1(CI = 15.5,16.7)).

https://doi.org/10.1371/journal.pmen.0000046.g002

https://doi.org/10.1371/journal.pmen.0000046.t002

Qualitative findings

Types of coping strategies/practices..

Of the 581 students who were categorised as marginalised, 351 (60.41%) responses provided sufficient data (i.e., more than one-word responses) for the qualitative content analysis. To understand how students from marginalised groups cope with stressful life events, the excerpts were analysed and organised into six subthemes that aligned with the three overarching domains of emotion-focused, problem-focused, and avoidant coping. Coping strategies/practices mainly involved talking to friends and family, practising religion or spirituality, engaging in creative/innovative activities, using entertainment as a distraction, waiting to see if things improve and isolating. (n) represents the number of quotes that were identified in the dataset to support each subtheme.

Emotion-focused coping.

Students described accessing social support to vent and seek comfort and advice. Although a smaller number of students used religious or spiritual practices like meditation or prayer to cope with stress (n = 20 example quotes present in the dataset), students most often referred to seeking support from friends and family members (n = 222).

Talking to friends and family . The primary source of support appeared to be romantic partners, close family members or friends, and fellow students. Students often spoke of meeting up with friends or family in person, but several students mentioned talking on the phone to express themselves and ‘get things off their minds’. For example, two students expressed;

“ My friends are my first port of call and after that my family . ” (White bisexual female with special education needs/disabilities) “ My partner is a source of support as are my parents … ” (Mixed-race male mature student)

Practising religion or spirituality . A small number of students relied on religious or spiritual practices to cope with stress. Common practices included prayers, meditation, and yoga.

“… My religion often helps improve my mental health . I often find myself turning to my religion (Islam) . It’s very comforting … ” (Mixed-race Muslim female mature student) “ I like to participate in yoga to provide calm and clarity . ” (Asian Hindu male) “… I practice a lot of mindfulness , such as breathing and meditating which helps me to cope in the moment . ” (White female with special education needs/disabilities) “ I meditate … ” (Black female who immigrated to the UK)

Problem-focused coping.

Students described actively coping with stressors using different techniques. A small number of students reported taking prescribed medications (n = 13) to help them cope, and a few others also described taking time to think things through (n = 14) or using positive reframing to ‘ look at the bright side’ (n = 3). However, the most common strategy/practice was to engage in creative/innovative activities (n = 61) as a form of actively coping with stress.

Engaging in creative/innovative activities . Several students reported using creative and innovative activities such as technology-based tools like social media platforms or spending time outdoors to cope with stressors. Among these responses, it was common for students to report using diverse combinations of creative activities. For example;

“ I usually rely on well-established coping mechanisms such as exercising , playing my guitar and journaling … ” (White female immigrant with special education needs/disabilities) “… I focus on my hobbies like drawing and painting to wind down … ” (White bisexual male immigrant) “… I will go out for a walk in the woods to help ground me … I use an app … that helps you with self-care techniques and that helps prevent me from feeling terrible and stressed . ” (White female bisexual mature student with special education needs/disabilities)

Avoidant coping.

Some students described avoiding dealing with stressors altogether by using distractions to help them disengage. This approach sometimes included unhelpful strategies like self-harming (n = 1) and substance use (n = 4). However, the more popular strategies/practices were using entertainment as a distraction (n = 28), waiting to see if things improved on their own (n = 24) and isolating (n = 19).

Using entertainment as a distraction . Some students chose to watch television, play computer games, read or listen to podcasts as a way of distracting themselves from life’s challenges. Other students also discussed how distractions in the form of entertainment temporarily helped them ignore what was happening around them. For the most part, students who described using entertainment as a distraction mentioned they chose this method as they found it difficult to talk to others about their problems.

“ I will listen to an audiobook or podcast that makes me feel better or play meditative music on YouTube . I find it hard to talk to therapists . I don’t know where to begin . ” (Black female immigrant) “ Nothing but YouTube makes me happier; it’s a good distraction” (Student who identified as They/Them , Genderless , Unsure about their sexuality and had special education needs/disabilities)

Waiting to see if things improve . Many students expressed that they mostly avoided confronting their problems by ‘waiting things out’ by sleeping in the hope that things improve by themselves. Again, some students disclosed this approach was easier as they struggled to reach out to others. For example;

“I don’t do anything about it , I believe sleep just helps me pull through … ” (Black Muslim male) “ I don’t do anything; just wait and see . ” (Mixed-race Female immigrant mature student) “ I don’t really do anything . I just wait for it to pass . ” (Female with special education needs/disabilities)

Isolating . A number of students described self-isolation as a form of avoidance of facing the reality of their life. Students generally expressed that they kept things to themselves and avoided reaching out to others for support. Two students narrated;

“ I tend to keep things bottled up … ” ( White male mature student with special education needs/disabilities ) “ I do not turn to anyone else [because] I’ve got trust issues … ” ( Black bisexual immigrant )

In contrast, other students found self-isolation helpful to provide an element of self-care and self-preservation in order to ‘refuel’.

“… I’m an introvert and I find time alone helps me recharge a bit” (White bisexual mature student) “ I prefer to just rest by myself rather than being around others” (Asian bisexual female with special education needs/disabilities)

Support needs.

To identify the mental health support needs among students from marginalised groups, the excerpts were analysed and organised into six subthemes that align with three overarching themes that centred around the need for improved or tailored well-being services, appropriate social support, and additional academic support. Support needs mainly involved increased awareness of mental health services, use of contemporary approaches to support mental health, frequent mentor/personal tutor support, opportunities for organised peer support, lowered academic pressures and reasonable adjustments.

Improved/Tailored mental health services.

Although several students generally acknowledged that well-being services were available at their colleges and universities, students wanted some improvements which centred around increased awareness of the available support (n = 94) and more novel and contemporary approaches for accessing help (n = 69).

Increased awareness of mental health support

Students generally expressed a need for mental health support services to be more integrated into other parts of the academic process to increase opportunities for students to understand more about mental health and the available mental health support. Even when some students were aware of the services, such as counselling, they were not aware of alternatives that were suitable for different levels of stress or mental health problems. The following statements reflect the views of two students;

“ I had no idea how or who to contact at uni until recently and they don’t really make it clear what they can offer you … ” (White pansexual transgendered student who identified as they/them) “… No one speaks about it . ” (White bisexual female mature student)

To increase awareness, students made recommendations like frequent announcements to their email addresses, letters, or seminars. As illustrated by the following responses:

“ Carry out seminars and webinars to create awareness on mental health and wellbeing” (Black male mature student) “… universities should send emails , letters or videos about how to improve mental health and let students know that they are there to support us all the time . ” (Muslim student with special education needs/disabilities)

Use of contemporary approaches to support mental health . Our analysis also highlighted a desire for alternative approaches to mental health support that appear to be more contemporary or different to traditional services. Students also described wanting more understanding and consideration from staff about their circumstances. This sometimes meant that students wanted tailored services to support minority groups. For example;

“ They [services] should be more understanding of students’ struggle because I am bisexual” (White bisexual female mature student) “ The support has been excellent but is perhaps tailored more to younger students” (White female mature student with special education needs/disabilities)

Once students highlighted what could be improved, they proceeded to suggest various ways that services could support mental health. This included wanting increased accessibility to services and increased variety in what is delivered and how support is offered.

“ The only issue is the waiting list and limited number of sessions they can provide , can’t this be more flexible?” (White pansexual female) “ Anonymous online support systems that are free will be nice” (White bisexual female with special education needs/disabilities)

Appropriate social support.

Students generally appreciated the therapeutic support received from professionals at well-being services in educational settings. However, due to long waiting times and the fact students sometimes felt their problems were not severe enough to receive counselling, students expressed a need for other forms of social support like regular mentor/personal tutor support (= 39) and more opportunities for organised peer support (n = 17).

Regular mentor/personal tutor support . In terms of social support, students felt like they would benefit from opportunities for regular support sessions with personal tutors/mentors to enable them to form a positive rapport, subsequently encouraging discussions around mental health. One student expressed;

“ There should be semester check-ups with personal tutors or similar for every student” (White bisexual female)

Opportunities for organised peer support . Some students also discussed how creating a community environment within the university, such as organised social-emotional groups, may help encourage therapeutic conversations with fellow peer groups. Students expressed a need for more opportunities to share experiences with like-minded peers in the hope that it may improve their wellbeing.

“ I wish that there were more chances to openly discuss mental health with other students like me , more group-therapy type of environments … ” (Immigrant student with special education needs/disabilities)

Additional academic support.

Students expressed that their academic work kept them busy and sometimes acted as a distraction from other stressors in their lives. However, students expressed that academic pressures like strict deadlines sometimes led to mental health problems. This was further compounded by the inability of some tutors to show empathy during stressful periods. Therefore, to promote mental health and wellbeing, students wanted institutions to lower academic pressures placed on students (n = 39) and provide reasonable academic adjustments when needed (n = 32).

Lowered academic pressures . Since students expressed that academic pressure can be a source of stress, students suggested that lessening the pressures placed on assignments and shifting focus on wider learning would help to alleviate the stress. Thus. students wanted more empathy. This was summed up in one student’s response;

“ I think the process of gaining education has such a big impact on my levels of stress and wellbeing . If they [place of study] place stronger effort on students’ mental health and enjoyment , then this could lower the pressures . ” (White bisexual male with special education needs/disabilities)

Reasonable academic adjustments . In addition to lowering academic pressures, students wanted reasonable adjustments offered by tutors to ease stress. The most common adjustment mentioned was extensions on assignments. Other adjustments included online streaming of classes and flexibility with deadlines and learning. The following quotes illustrated this;

“ They should offer better measures like leeway with deadlines sometimes if you’re stressed . ” (White immigrant female) “ Education should not worsen mental health situations . Education providers should try to find reasonable adjustments that can be made to assist , for example , allowing me to stream lectures from home … ” (Asexual transgender student with special education needs/disabilities)

Integration of findings

Overall, students who were categorised as marginalised utilised a range of coping strategies to support their mental health and wellbeing. The overlap of social categories (i.e., intersectionality) appeared to result in increasing efforts to cope with life stressors. The popular coping strategies practised generally appeared to be harmless. For example, students viewed talking to friends and family members as a safe space to share worries and obtain support. However, there was a need for increased awareness of mental health services and the use of contemporary approaches to support mental health. At the integration level, the findings highlighted coping strategies and support needs that suggest the need to offer personalised, culturally appropriate early interventions to students at risk of experiencing mental health challenges ( Fig 3 ).

https://doi.org/10.1371/journal.pmen.0000046.g003

Summary of findings

The aims of this study were two-fold. First, to assess and understand the types and prevalence of different coping strategies/practices adopted by marginalised students. From the UK-based sample of 788 students, we observed that students who were marginalised based on one or more factors (n = 581) had significantly higher scores for problem-focused, emotion-focused and avoidant coping compared to other students (n = 207). Coping strategies/practices included talking to friends and family, practising religion or spirituality, engaging in creative/innovative activities, using entertainment as a distraction, waiting to see if things improve and isolating.

The second objective was to explore the mental health support needs of marginalised students. Based on our analysis of 351 cases, students mainly expressed a need for improved or tailored services, additional academic support, and appropriate social support. These included increased awareness of available mental health support, contemporary approaches to support mental health, regular mentor/personal tutor meetings, lowered academic pressures, reasonable academic adjustments when necessary and opportunities for organised peer support.

To the best of our knowledge, this is the only study to analyse a large sample (N = 581) of further and higher education students from multiple marginalised groups in the UK. Compared to previous research [ 63 – 66 ], our study is also novel as it pooled the data from multiple groups with multiple characteristics, which helped explore understudied areas like intersectionality. This study also analysed data from marginalised groups such as mature students, immigrants, and specific religious groups where there is a lack of mental health research among college and university students [ 67 , 68 ].

Interpretation of our findings

Our findings align with the existing evidence suggesting that students from marginalised groups adopt a wealth of mental health coping strategies, such as, talking to friends and family and religious or spiritual practices to promote acceptance, belonging and feelings of safety and security [ 69 – 71 ]. It is possible that the unique experiences of students from marginalised groups [ 23 ] resulted in a need for increased coping mechanisms, as highlighted in the current study. This may also explain the desire for appropriate social support. For example, peer-to-peer support, such as talking to other students and sharing experiences, supports socialising, which is fundamental in promoting wellbeing among students [ 72 ]. However, Gladstone et al [ 73 ] highlighted that marginalised groups may have reduced opportunities to develop these positive peer relationships due to negative experiences such as bullying or social exclusion within education settings [ 73 ]. As a result, it becomes important to provide such opportunities as part of an educational provision that is culturally aware of the needs of marginalised groups.

Our findings are also in line with previous research suggesting that religion and prayer can be prevalent coping strategies among marginalised groups and may act as a stress buffer while offering support, security and safety to students who may not have access to other services [ 74 ]. It is also well established that due to some cultural beliefs, some students may also seek support from religious leaders before accessing mental health services [ 75 ]. Previous research also concluded that stigma may still be one of the primary reasons some students may not seek support from others [ 76 ]. Therefore, this may further explain alternative coping strategies like isolation or waiting to see if things improve, which our study highlighted. Notably, our findings suggested that students may engage in creative or innovative activities as a form of self-help and use entertainment as a source of distraction to help cope with stress. Our findings build on previous research highlighting creativity and innovative activities such as hobbies and social media as useful strategies to support mental health and wellbeing [ 77 , 79 ]. Yet creative and innovative activities with important therapeutic potential remain largely untapped within an educational environment. There is growing evidence suggesting that social media can be a protective factor against loneliness and impaired mental health [ 77 ]. Social media and online gaming are widely used among the student population, and therefore, this can also be a way to connect with people of similar interests, which may contribute to feelings of acceptance and belonging [ 78 ]. Other forms of creativity, like engaging with music and art, have also shown strong evidence for supporting mental health and wellbeing [ 79 – 82 ]. Thus, in the absence of social or professional connections, students from marginalised groups may use these strategies as a source of support. This also highlights further opportunities to incorporate creativity and physical activity among groups of students to encourage socialising and promote self-care and a sense of belongingness. These alternatives to verbal approaches could also help overcome language and cultural barriers that some students experience [ 83 ].

Our findings also add to the calls for increased awareness of mental health and mental health support among marginalised groups [ 84 , 85 ]. Prior studies have also noted that increased awareness of mental health services may have a positive impact on mental health and wellbeing [ 27 , 86 , 87 ]. This, alongside difficulties in accessing appropriate services, can make it difficult for some students to seek professional help [ 88 ]. Therefore, more information about the types of services available and how to access them seems to be a recommendation for improved service provision among marginalised students in our sample. Some students highlighted that speaking to a counsellor was the only service they were aware of, and therefore, they did not always see their problems as requiring that level of support. This is an important finding suggesting that existing services may need to adopt new ways of sharing information about all services in addition to using diverse ways of delivery, such as the use of technology [ 89 , 90 ]. This may also coincide with calls for more appropriate services, which are offered through online mediums to meet the preferences of some students. Similarly, students may benefit from alternative support from other professionals through mentorship and coaching [ 91 ]. It appears that students may not always have access or knowledge of this range of support [ 92 ]. To ensure appropriate services are offered, there have been several policy and advocacy calls to co-design support to ensure services are adapted to suit the needs of marginalised groups [ 89 , 93 ]. This understanding is the foundation for developing and implementing more targeted and localised support mechanisms and interventions.

Our findings further suggest that regular mentor/personal tutor meetings may be a welcomed approach for supporting students’ mental health. Watts [ 92 ] highlighted that the personal tutor plays a pivotal role in shaping the student’s university experience, representing a focal point in the student’s interaction with the institution [ 94 ]. A study by Yale (2019) found that establishing an authentic and positive connection with one’s personal tutor was discovered to act as a protective factor, mitigating certain challenges faced during the first year of university and fostering a stronger sense of belonging [ 95 ].

Another expressed need was lowered academic pressures. In line with theories by Maslow (1943) and Rogers (1992), students may need psychological safety prior to focusing on and achieving academic success [ 40 , 41 ]. Therefore, considerations for lowering academic pressures by introducing measures such as academic adjustments (e.g., assignment extensions, further assignment support, alternative assessment options) may be welcomed by students. This finding highlights the importance of creating a supportive environment so marginalised students can thrive academically and personally.

Implications for practice, policy and future research

The voices of marginalised students represented in this study indicated the need for enhanced individualised services and academic and social support to better cope with stress and mental health issues. Undeniably, this may require student support services to increase their provision to offer more tailored services so all students can benefit [ 38 , 39 ]. Therefore, to address this growing gap, more efficient strategies and efforts are also needed to implement sustainable approaches. Watkins et al (2021) propose a stepped care model where the intervention starts with an intensive, least resource, universal or whole university approach followed by targeted interventions [ 96 , 97 ]. This model resonates with our study since marginalised students reported higher problem-focused coping strategies than other students. This could imply that these students are already utilising facets of active coping such as the use of informational support, planning, and positive reframing. Henceforth, promoting these inherent strengths, self-initiated care, and responsibility from students could facilitate a positive shift in the university culture from the ground up. To achieve this, education providers can aim to facilitate an environment for a compassionate and mindful education culture and nurture green spaces for engaging in creative, social and physical activities and other forms of self-care [ 4 ]. To tackle health inequities and offer support to students with severe difficulties, improved pathways to wellbeing services and tailored counselling and therapies are also necessary. This might include strategies designed to expand academic and social support initiatives and measures to address the needs highlighted by the participants of this study. However, it is not yet clear what approaches work best for whom as models of care vary across institutions and they have not been methodically evaluated rigorously [ 98 ]. Further research is needed to test the effective ways of offering universal and targeted care delivery to students and identify accessible pathways. Therefore, ongoing research may also be needed to co-design and evaluate new services and interventions as they are implemented.

Our findings also have implications for existing and revised guidance related to student wellbeing [ 99 – 101 ]. Whilst there is a growing body of advice provided for schools and colleges, the statutory and policy elements for higher education settings are still limited. The findings in this study highlight the importance of understanding the diversity of mental health needs and the importance of valuing insights regarding solutions to inform policy development. The argument for these changes can easily be made with respect to efforts to improve diversity and address social injustice amongst college and university student support services. Indeed, without strategies to widen the inclusivity of these services, there is a risk that they will, somewhat ironically, continue to exacerbate the challenges experienced by a large number of students.

Limitations

Despite the strengths of this study, some limitations should be acknowledged. As this was a subsample of a larger dataset, the authors relied on the scope, quality and validity of the data from the original study [ 46 ]. Hence, since the primary study was conducted online, our findings may have a bias towards participants who have access to digital devices. Therefore the responses may not be representative of all marginalised students (e.g., low socioeconomic groups). We also acknowledge that mental health problems could have been heightened during the period of data collection based on the Covid-19 pandemic, so our findings may not be fully representative of the average daily stressors. Furthermore, within the qualitative data, the depth of information was limited as students replied to open-ended questions online, which meant some responses were brief. Further in-depth interviews can explore some of the themes raised in this study across marginalised groups and/or offer further information and understanding of specific subgroups. Although we pooled data from multiple groups with various characteristics, we acknowledge that individuals may attribute different meanings to each coping strategy and expressed need. Similarly, by exploring the mental health coping strategies and needs of specific subgroups, some individualised interventions could cause further discrimination. Therefore, it is important that codesign principles are adopted during intervention development, evaluation and implementation. Another limitation of this study is that we relied on self-report characteristics which meant it may not have been possible for some students to accurately identify as having SEN/D.

Conclusions

This study described, for the first time, the mental health support needs and mental health coping strategies/practices of a large number of marginalised further and higher education students in the UK, bringing together the voices of diverse groups with diverse needs. We found that students who were marginalised based on one or more characteristics had significantly higher scores for problem-focused, emotion-focused and avoidant coping than other students. We also found that students needed improved or tailored services, additional academic support, and appropriate social support. These findings provide a unique and important knowledge base to inform policies, practice and future research to support typically underserved and underrepresented students in the UK.

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• Open access
• Published: 26 August 2020

Understanding the mental health of doctoral researchers: a mixed methods systematic review with meta-analysis and meta-synthesis

• Cassie M. Hazell   ORCID: orcid.org/0000-0001-5868-9902 1 ,
• Laura Chapman 2 ,
• Sophie F. Valeix 3 ,
• Paul Roberts 4 ,
• Jeremy E. Niven 5 &
• Clio Berry 6  

Systematic Reviews volume  9 , Article number:  197 ( 2020 ) Cite this article

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Data from studies with undergraduate and postgraduate taught students suggest that they are at an increased risk of having mental health problems, compared to the general population. By contrast, the literature on doctoral researchers (DRs) is far more disparate and unclear. There is a need to bring together current findings and identify what questions still need to be answered.

We conducted a mixed methods systematic review to summarise the research on doctoral researchers’ (DRs) mental health. Our search revealed 52 articles that were included in this review.

The results of our meta-analysis found that DRs reported significantly higher stress levels compared with population norm data. Using meta-analyses and meta-synthesis techniques, we found the risk factors with the strongest evidence base were isolation and identifying as female. Social support, viewing the PhD as a process, a positive student-supervisor relationship and engaging in self-care were the most well-established protective factors.

Conclusions

We have identified a critical need for researchers to better coordinate data collection to aid future reviews and allow for clinically meaningful conclusions to be drawn.

Systematic review registration

PROSPERO registration CRD42018092867

Peer Review reports

Student mental health has become a regular feature across media outlets in the United Kingdom (UK), with frequent warnings in the media that the sector is facing a ‘mental health crisis’ [ 1 ]. These claims are largely based on the work of regulatory authorities and ‘grey’ literature. Such sources corroborate an increase in the prevalence of mental health difficulties amongst students. In 2013, 1 in 5 students reported having a mental health problem [ 2 ]. Only 3 years later, however, this figure increased to 1 in 4 [ 3 ]. In real terms, this equates to 21,435 students disclosing mental health problems in 2013 rising to 49,265 in 2017 [ 4 ]. Data from the Higher Education Statistics Agency (HESA) demonstrates a 210% increase in the number of students terminating their studies reportedly due to poor mental health [ 5 ], while the number of students dying by suicide has consistently increased in the past decade [ 6 ].

This issue is not isolated to the UK. In the United States (US), the prevalence of student mental health problems and use of counselling services has steadily risen over the past 6 years [ 7 ]. A large international survey of more than 14,000 students across 8 countries (Australia, Belgium, Germany, Mexico, Northern Ireland, South Africa, Spain and the United States) found that 35% of students met the diagnostic criteria for at least one common mental health condition, with highest rates found in Australia and Germany [ 8 ].

The above figures all pertain to undergraduate students. Finding equivalent information for postgraduate students is more difficult, and where available tends to combine data for postgraduate taught students and doctoral researchers (DRs; also known as PhD students or postgraduate researchers) (e.g. [ 4 ]). The latest trend analysis based on data from 36 countries suggests that approximately 2.3% of people will enrol in a PhD programme during their lifetime [ 9 ]. The countries with the highest number of DRs are the US, Germany and the UK [ 10 ]. At present, there are more than 281,360 DRs currently registered across these three countries alone [ 11 , 12 ], making them a significant part of the university population. The aim of this systematic review is to bring attention specifically to the mental health of DRs by summarising the available evidence on this issue.

Using a mixed methods approach, including meta-analysis and meta-synthesis, this review seeks to answer three research questions: (1) What is the prevalence of mental health difficulties amongst DRs? (2) What are the risk factors associated with poor mental health in DRs? And (3) what are the protective factors associated with good mental health in DRs?

Literature search

We conducted a search of the titles and abstracts of all article types within the following databases: AMED, BNI, CINAHL, Embase, HBE, HMIC, Medline, PsycInfo, PubMed, Scopus and Web of Science. The same search terms were used within all of the databases, and the search was completed on the 13th April 2018. Our search terms were selected to capture the variable terms used to describe DRs, as well as the terms used to describe mental health, mental health problems and related constructs. We also reviewed the reference lists of all the papers included in this review. Full details of the search strategy are provided in the supplementary material .

Inclusion criteria

Articles meeting the following criteria were considered eligible for inclusion: (1) the full text was available in English; (2) the article presented empirical data; (3) all study participants, or a clearly delineated sub-set, were studying at the doctoral level for a research degree (DRs or equivalent); and (4) the data collected related to mental health constructs. The last of these criteria was operationalised (a) for quantitative studies as having at least one mental health-related outcome measure, and (b) for qualitative studies as having a discussion guide that included questions related to mental health. We included university-published theses and dissertations as these are subjected to a minimum level of peer-review by examiners.

Exclusion criteria

In order to reduce heterogeneity and focus the review on doctoral research as opposed to practice-based training, we excluded articles where participants were studying at the doctoral level, but their training did not focus on research (e.g. PsyD doctorate in Clinical Psychology).

Screening articles

Papers were screened by one of the present authors at the level of title, then abstract, and finally at full text (Fig. 1 ). Duplicates were removed after screening at abstract. At each level of screening, a random 20% sub-set of articles were double screened by another author, and levels of agreement were calculated (Cohen’s kappa [ 13 ]). Where disagreements occurred between authors, a third author was consulted to decide whether the paper should or should not be included. All kappa values evidence at least moderate agreement between authors [ 14 ]—see Fig. 1 for exact kappa values.

PRISMA diagram of literature review process

Data extraction

This review reports on both quantitative and qualitative findings, and separate extraction methods were used for each. Data extraction was performed by authors CH, CB, SV and LC.

Quantitative data extraction

The articles in this review used varying methods and measures. To accommodate this heterogeneity, multiple approaches were used to extract quantitative data. Where available, we extracted (a) descriptive statistics, (b) correlations and (c) a list of key findings. For all mental health outcome measures, we extracted the means and standard deviations for the DR participants, and where available for the control group (descriptive statistics). For studies utilising a within-subjects study design, we extracted data where a mental health outcome measure was correlated with another construct (correlations). Finally, to ensure that we did not lose important findings that did not use descriptive statistics or correlations, we extracted the key findings from the results sections of each paper (list of key findings). Key findings were identified as any type of statistical analysis that included at least one mental health outcome.

Qualitative data extraction

In line with the meta-ethnographic method [ 15 ] and our interest in the empirical data as well as the authors’ interpretations thereof, i.e. the findings of each article [ 16 ], the data extracted from the articles comprised both results/findings and discussion/conclusion sections. For articles reporting qualitative findings, we extracted the results and discussion sections from articles verbatim. Where articles used mixed methods, only the qualitative section of the results was extracted. Methodological and setting details from each article were also extracted and provided (see Appendix A) in order to contextualise the studies.

Data analysis

Quantitative data analysis, descriptive statistics.

We present frequencies and percentages of the constructs measured, the tools used and whether basic descriptive statistics ( M and SD ) were reported. The full data file is available from the first author upon request.

Effect sizes

Where studies had a control group, we calculated a between-group effect size (Cohen’s d ) using the formula reported by Wilson [ 17 ], and interpreted using the standard criteria [ 13 ]. For all other studies, we sought to compare results with normative data where the following criteria were satisfied: (a) at least three studies reported data using the same mental health assessment tool; (b) empirical normative data were available; and (c) the scale mean/total had been calculated following original authors’ instructions. Only the Perceived Stress Scale (PSS) 10- [ 18 ] and 14-item versions [ 19 ] met these criteria. Normative data were available from a sample of adults living in the United States: collected in 2009 for the 10-item version ( n = 2000; M = 15.21; SD = 7.28) [ 20 ] and in 1983 for the 14-item version ( n = 2355; M = 19.62; SD = 7.49) [ 18 ].

The meta-analysis of PSS data was conducted using MedCalc [ 21 ], and based on a random effects model, as recommended by [ 22 ]. The between-group effect sizes (DRs versus US norms) were calculated comparing PSS means and standard deviations in the respective groups. The effect sizes were weighted using the variable variances [ 23 ].

Correlations

Where at least three studies reported data reflecting a bivariate association between a mental health and another variable, we summarised this data into a meta-analysis using the reported r coefficients and sample sizes. Again, we used MedCalc [ 21 ] to conduct the analysis using a random effects model, based on the procedure outlined by Borenstein, Hedges, Higgins and Rothstein [ 24 ]. This analysis approach involves converting correlation coefficients into Fisher’s z values [ 25 ], calculating the summary of Fisher’s z , and then converting this to a summary correlation coefficient ( r ). The effect sizes were weighted in line with the Hedges and Ollkin [ 23 ] method. Heterogeneity was assessed using the Q statistic, and I 2 value—both were interpreted according to the GRADE criteria [ 26 ]. Where correlations could not be summarised within a meta-analysis, we have reported these descriptively.

Due to the heterogenous nature of the studies, the above methods could not capture all of the quantitative data. Therefore, additional data (e.g. frequencies, statistical tests) reported in the identified articles was collated into a single document, coded as relating to prevalence, risk or protective factors and reported as a narrative review.

Qualitative data analysis

We used thematic analytic methods to analyse the qualitative data. We followed the thematic synthesis method [ 16 , 27 ] and were informed by a thematic analysis approach [ 28 , 29 ]. We took a critical realist epistemological stance [ 30 , 31 ] and aimed to bring together an analysis reflecting meaningful patterns amongst the data [ 29 ] or demi-regularities, and identifying potential social mechanisms that might influence the experience of such phenomena [ 31 ]. The focus of the meta-synthesis is interpretative rather than aggregative [ 32 ].

Coding was line by line, open and complete. Following line-by-line coding of all articles, a thematic map was created. Codes were entered on an article-by-article basis and then grouped and re-grouped into meaningful patterns. Comparisons were made across studies to attempt to identify demi-regularities or patterns and contradictions or points of departure. The thematic map was reviewed in consultation with other authors to inductively create and refine themes. Thematic summaries were created and brought together into a first draft of the thematic structure. At this point, each theme was compared against the line-by-line codes and the original articles in order to check its fit and to populate the written account with illustrative quotations.

Research rigour

The qualitative analysis was informed by independent coding by authors CB and SV, and analytic discussions with CH, SV and LC. Our objective was not to capture or achieve inter-rater reliability, rather the analysis was strengthened through involvement of authors from diverse backgrounds including past and recent PhD completion, experiences of mental health problems during PhD completion, PhD supervision experience, experience as employees in a UK university doctoral school and different nationalities. In order to enhance reflexivity, CB used a journal throughout the analytic process to help notice and bracket personal reflections on the data and the ways in which these personal reflections might impact on the interpretation [ 29 , 33 ]. The ENTREQ checklist [ 34 ] was consulted in the preparation of this report to improve the quality of reporting.

Quality assessment

Quantitative data.

The quality of the quantitative papers was assessed using the STROBE combined checklist [ 35 ]. A random 20% sub-sample of these studies were double-coded and inter-rater agreement was 0.70, indicating ‘substantial’ agreement [ 14 ]. The maximum possible quality score was 23, with a higher score indicating greater quality, with the mean average of 15.97, and a range from 0 to 22. The most frequently low-scoring criteria were incomplete reporting regarding the management of missing data, and lack of reported efforts to address potential causes of bias.

Qualitative data

There appeared to be no discernible pattern in the perceived quality of studies; the highest [ 36 , 37 , 38 , 39 , 40 ] and lowest scoring [ 41 , 42 , 43 , 44 , 45 , 46 ] studies reflected both theses and journal publications, a variety of locations and settings and different methodologies. The most frequent low-scoring criteria were relating to the authors’ positions and reflections thereof (i.e. ‘Qualitative approach and research paradigm’, ‘Researcher characteristics and reflexivity’, ‘Techniques to enhance trustworthiness’, ‘Limitations’, ‘Conflict of interest and Funding’). Discussions of ethical issues and approval processes was also frequently absent. We identified that we foregrounded higher quality studies in our synthesis in that these studies appeared to have greater contributions reflected in the shape and content of the themes developed and were more likely to be the sources of the selected illustrative quotes.

Mixed methods approach

The goal of this review is to answer the review questions by synthesising the findings from both quantitative and/or qualitative studies. To achieve our goal, we adopted an integrated approach [ 47 ], whereby we used both quantitative and qualitative methods to answer the same review question, and draw a synthesised conclusion. Different analysis approaches were used for the quantitative and qualitative data and are therefore initially reported separately within the methods. A separate synthesised summary of the findings is then provided.

Overview of literature

Of the 52 papers included in this review (Table 1 ), 7 were qualitative, 29 were quantitative and 16 mixed methods. Most articles (35) were peer-reviewed papers, and the minority were theses (17). Only four of the articles included a control group; in three instances comprising students (but not DRs) and in the other drawn from the general population.

Quantitative results

Thirty-five papers reported quantitative data, providing 52 reported sets of mental health related data (an average of 1.49 measures per study): 24 (68.57%) measured stress, 10 (28.57%) anxiety, 9 (25.71%) general wellbeing, 5 (14.29%) social support, 3 (8.57%) depression and 1 (2.86%) self-esteem. Five studies (9.62%) used an unvalidated scale created for the purposes of the study. Fifteen studies (28.85%) did not report descriptive statistics.

Of the four studies that included a control group, only two of these reported descriptive statistics for both groups on a mental health outcome [ 66 , 69 ]. There is a small (Cohen’s d = 0.27) and large between-group effect (Cohen’s d = 1.15) when DRs were compared to undergraduate and postgraduate clinical psychology students respectively in terms of self-reported stress.

The meta-analysis of DR scores on the PSS (both 10- and 14-item versions) compared to population normative data produced a large and significant between-group effect size ( d = 1.12, 95% CI [0.52, 1.73]) in favour of DRs scoring higher on the PSS than the general population (Fig. 2 ), suggesting DRs experience significantly elevated stress. However, these findings should be interpreted in light of the significant between-study heterogeneity that can be classified as ‘considerable’ [ 26 ].

A meta-analysis of between-group effect sizes (Cohen’s d ) comparing PSS scores (both 10- and 14-item versions) from DRs and normative population data. *Studies using the 14 item version of the PSS; a positive effect size indicates DRs had a higher score on the PSS; a negative effect size indicates that the normative data produced a higher score on the PSS; black diamond = total effect size (based on random effects model); d = Cohen’s d ; Q = heterogeneity; Z = z score; I 2 = proportion of variance due to between-study heterogeneity; p = exact p value

To explore this heterogeneity, we re-ran the meta-analysis separately for the 10- and 14-item versions. The effect size remained large and significant when looking only at the studies using the 14-item version ( k = 6; d = 1.41, 95% CI [0.63, 2.19]), but was reduced and no longer significant when looking at the 10-item version only ( k = 3; d = 0.57, 95% CI [− 0.51, 1.64]). However, both effect sizes were still marred by significant heterogeneity between studies (10-item: Q = 232.02, p < .001; 14-item: Q = 356.76, p < .001).

Studies reported sufficient correlations for two separate meta-analyses; the first assessing the relationship between stress (PSS [ 18 , 19 ]) and perceived support, and the second between stress (PSS) and academic performance.

Stress x support

We included all measures related to support irrespective of whom that support came from (e.g. partner support, peer support, mentor support). The overall effect size suggests a small and significant negative correlation between stress and support ( r = − .24, 95% CI [− 0.34, − 0.13]) (see Fig. 3 ), meaning that low support is associated with greater perceived stress. However, the results should be interpreted in light of the significant heterogeneity between studies. The I 2 value quantifies this heterogeneity as almost 90% of the variance being explained by between-study heterogeneity, which is classified as ‘substantial’ (26).

Forest plot and meta-analysis of correlation coefficients testing the relationship between stress and perceived support. Black diamond = total effect size (based on random effects model); r = Pearson’s r ; Q = heterogeneity; Z = z score; I 2 = proportion of variance due to between-study heterogeneity; p = exact p value

Stress x performance

The overall effect size suggests that there is no relationship between stress and performance in their studies ( r = − .07, 95% CI [− 0.19, 0.05]) (see Fig. 4 ), meaning that DRs perception of their progress was not associated with their perceived stress This finding suggests that the amount of progress that DRs were making during their studies was not associated with stress levels.

Forest plot and meta-analysis of correlation coefficients testing the relationship between stress and performance. Black diamond = total effect size (based on random effects model); r = Pearson’s r ; Q = heterogeneity; Z = z score; I 2 = proportion of variance due to between-study heterogeneity; p = exact p value

Other correlations

Correlations reported in less than three studies are summarised in Fig. 5 . Again, stress was the most commonly tested mental health variable. Self-care and positive feelings towards the thesis were consistently found to negatively correlate with mental health constructs. Negative writing habits (e.g. perfectionism, blocks and procrastination) were consistently found to positively correlate with mental health constructs. The strongest correlations were found between stress, and health related quality of life ( r = − .62) or neuroticism ( r = .59), meaning that lower stress was associated with greater quality of life and reduced neuroticism. The weakest relationships ( r < .10) were found between mental health outcomes and: faculty concern, writing as knowledge transformation, innate writing ability (stress and anxiety), years married, locus of control, number of children and openness (stress only).

Correlation coefficients testing the relationship between a mental health outcome and other construct. Correlation coefficients are given in brackets ( r ); * p < .05; each correlation coefficient reflects the results from a single study

Several studies reported DR mental health problem prevalence and this ranged from 36.30% [ 54 ] to 55.9% [ 67 ]. Using clinical cut-offs, 32% were experiencing a common psychiatric disorder [ 64 ]; with another study finding that 53.7% met the questionnaire cut-off criteria for depression, and 41.9% for anxiety [ 67 ]. One study compared prevalence amongst DRs and the general population, employees and other higher education students; in all instances, DRs had higher levels of psychological distress (non-clinical), and met criteria for a clinical psychiatric disorder more frequently [ 64 ].

Risk factors

Demographics Two studies reported no significant difference between males and females in terms of reported stress [ 57 , 73 ], but the majority suggested female DRs report greater clinical [ 80 ], and non-clinical problems with their mental health [ 37 , 64 , 79 , 83 , 89 ].

Several studies explored how mental health difficulties differed in relation to demographic variables other than gender, suggesting that being single or not having children was associated with poorer mental health [ 64 ] as was a lower socioeconomic status [ 71 ]. One study found that mental health difficulties did not differ depending on DRs’ ethnicity [ 51 ], but another found that Black students attending ‘historically Black universities’ were significantly more anxious [ 87 ]. The majority of the studies were conducted in the US, but only one study tested for cross-cultural differences: reporting that DRs in France were more psychologically distressed than those studying in the UK [ 67 ].

Work-life balance Year of study did not appear to be associated with greater subjective stress in a study involving clinical psychology DRs (Platt and Schaefer [ 75 ]), although other studies suggested greater stress reported by those in the latter part of their studies [ 89 ], who viewed their studies as a burden [ 81 ], or had external contracts, i.e. not employed by their university [ 85 ]. Regression analyses revealed that a common predictor of poor mental health was uncertainty in DR studies; whether in relation to uncertain funding [ 64 ] or uncertain progress [ 80 ]. More than two-thirds of DRs reported general academic pressure as a cause of stress, and a lack of time as preventing them from looking after themselves [ 58 ]. Being isolated was also a strong predictor of stress [ 84 ].

Protective factors

DRs who more strongly endorsed all of the five-factor personality traits (openness, conscientiousness, extraversion, agreeableness and neuroticism) [ 66 ], self-reported higher academic achievement [ 40 ] and viewed their studies as a learning process (rather than a means to an end) [ 82 ] reported fewer mental health problems. DRs were able to mitigate poor mental health by engaging in self-care [ 72 ], having a supervisor with an inspirational leadership style [ 64 ] and building coping strategies [ 56 ]. The most frequently reported coping strategy was seeking support from other people [ 37 , 58 ].

Qualitative results

Meta-synthesis.

Four higher-order themes were identified: (1) Always alone in the struggle, (2) Death of personhood, (3) The system is sick and (4) Seeing, being and becoming. The first two themes reflect individual risk/vulnerability factors and the processes implicated in the experience of mental distress, the third represents systemic risk and vulnerability factors and the final theme reflects individual and systemic protective mechanisms and transformative influences. See Table 2 for details of the full thematic structure with illustrative quotes.

Always alone in the struggle

‘Always alone in the struggle’ reflects the isolated nature of the PhD experience. Two subthemes reflect different aspects of being alone; ‘Invisible, isolated and abandoned’ represents DRs’ sense of physical and psychological separation from others and ‘It’s not you, it’s me’ represents DRs’ sense of being solely responsible for their PhD process and experience.

Invisible, isolated and abandoned

Feeling invisible and isolated both within and outside of the academic environment appears a core DR experience [ 39 , 43 , 81 ]. Isolation from academic peers seemed especially salient for DRs with less of a physical presence on campus, e.g. part-time and distance students, those engaging in extensive fieldwork, outside employment and those with no peer research or lab group [ 36 , 52 , 68 ]. Where DRs reported relationships with DR peers, these were characterised as low quality or ‘not proper friendships’ and this appeared linked to a sense of essential and obvious competition amongst DRs with respect to current and future resources, support and opportunities [ 39 ], in which a minority of individuals were seen to receive the majority share [ 36 , 74 ]. Intimate sharing with peers thus appeared to feel unsafe. This reflected the competitive environment but also a sense of peer relationships being predicated on too shared an experience [ 39 ].

In addition to poor peer relations, a mismatch between the expected and observed depth of supervisor interest, engagement and was evident [ 40 , 81 ]. This mismatch was clearly associated with disappointment and anger, and a sense of abandonment, which appeared to impact negatively on DR mental health and wellbeing [ 42 ] (p. 182). Moreover, DRs perceived academic departments as complicit in their isolation; failing to offer adequate opportunities for academic and social belonging and connections [ 42 , 81 ] and including PGRs only in a fleeting or ‘hollow’ sense [ 37 ]. DRs identified this isolation as sending a broader message about academia as a solitary and unsupported pursuit; a message that could lead some DRs to self-select out of planning for future in academia [ 37 , 42 ]. DRs appeared to make sense of their lack of belonging in their department as related to their sense of being different, and that this difference might suggest they did not ‘fit in’ with academia more broadly [ 74 ]. In the short-term, DRs might expend more effort to try and achieve a social and/or professional connection and equitable access to support, opportunities and resources [ 74 ]. However, over the longer-term, the continuing perception of being professionally ‘other’ also seemed to undermine DRs’ sense of meaning and purpose [ 81 ] and could lead to opting out of an academic career [ 62 , 74 ].

Isolation within the PhD was compounded by isolation from one’s personal relationships. This personal isolation was first physical, in which the laborious nature of the PhD acted as a catalyst for the breakdown of pre-existing relationships [ 76 ]. Moreover, DRs also experienced a sense of psychological detachment [ 45 , 74 ]. Thus, the experience of isolation appeared to be extremely pervasive, with DRs feeling excluded and isolated physically and psychologically and across both their professional and personal lives.

It’s not you, it’s me

‘It’s not you, it’s me’ reflects DRs’ perfectionism as a central challenge of their PhD experience and a contributor to their sense of psychological isolation from other people. DRs’ perfectionism manifested in four key ways; firstly, in the overwhelming sense of responsibility experienced by DRs; secondly, in the tendency to position themselves as inadequate and inferior; thirdly, in cycles of perfectionist paralysis; and finally, in the tendency to find evidence which confirms their assumed inferiority.

DRs positioned themselves as solely responsible for their PhD and for the creation of a positive relationship with their supervisor [ 36 , 52 , 81 ]. DRs expressed a perceived need to capture their supervisors’ interest and attention [ 36 , 52 , 74 ], feeling that they needed to identify and sell to their supervisors some shared characteristic or interest in order to scaffold a meaningful relationship. DRs appeared to feel it necessary to assume sole responsibility for their personal lives and to prohibit any intrusion of the personal in to the professional, even in incredibly distressing circumstances [ 42 ].

DRs appeared to compare themselves against an ideal or archetypal DR and this comparison was typically unfavourable [ 37 ], with DRs contrasting the expected ideal self with their actual imperfect and fallible self [ 37 , 42 , 52 ]. DRs’ sense of inadequacy appeared acutely and frequently reflected back to them by supervisors in the form of negative or seemingly disdainful feedback and interactions [ 41 , 76 ]. DRs framed negative supervisor responses as a cue to work harder, meaning they were continually striving, but never reaching, the DR ideal [ 76 ]. This ideal-actual self-discrepancy was associated with a tendency towards punitive self-talk with clear negative valence [ 38 ].

DRs appear to commonly use self-castigation as a necessary (albeit insufficient) means to motivate themselves to improve their performance in line with perfectionistic standards [ 38 , 41 ]. The oscillation between expectation and actuality ultimately resulted in increased stress and anxiety and reduced enjoyment and motivation. Low motivation and enjoyment appeared to cause procrastination and avoidance, which lead to a greater discrepancy between the ideal and actual self; in turn, this caused more stress and anxiety and further reduced enjoyment and motivation leading to a sense of stuckness [ 76 ].

The internalisation of perceived failure was such that DRs appeared to make sense of their place, progress and possible futures through a lens of inferiority, for example, positioning themselves as less talented and successful compared to their peers [ 37 ]. Thus, instances such as not being offered a job, not receiving funding, not feeling connected to supervisors, feeling excluded by academics and peers were all made sense of in relation to DRs’ perceived relative inadequacy [ 36 ].

Death of personhood

The higher-order theme ‘Death of personhood’ reflects DRs’ identity conflict during the PhD process; a sense that DRs’ engage in a ‘Sacrifice of personal identity’ in which they feel they must give up their pre-existing self-identity, begin to conceive of themselves as purely ‘takers’ personally and professionally, thus experiencing the ‘Self as parasitic’, and ultimately experience a ‘Death of self-agency’ in relation to the thesis, the supervisor and other life roles and activities.

A sacrifice of personal identity

The sacrifice of personal identity first manifests as an enmeshment with the PhD and consequent diminishment of other roles, relationships and activities that once were integral to the DRs’ sense of self [ 59 , 76 ]. DRs tended to prioritise PhD activities to the extent that they engaged in behaviours that were potentially damaging to their personal relationships [ 76 ]. DRs reported a sense of never being truly free; almost physically burdened by the weight of their PhD and carrying with them a constant ambient guilt [ 37 , 38 , 44 , 76 ]. Time spent on non-PhD activities was positioned as selfish or indulgent, even very basic activities of living [ 76 ].

The seeming incompatibility of aspects of prior personal identity and the PhD appears to result in a sense of internal conflict or identity ‘collision’ [ 59 ]. Friends and relatives often provided an uncomfortable reflection of the DR’s changing identity, leaving DRs feeling hyper-visible and carrying the burden of intellect or trailblazer status [ 74 ]; providing further evidence for the incompatibility of their personal and current and future professional identities. Some DRs more purposefully pruned their relationships and social activities; to avoid identity dissonance, to conserve precious time and energy for their PhD work, or as an acceptance of total enmeshment with academic work as necessary (although not necessarily sufficient) for successful continuation in academia [ 40 , 52 , 77 ]. Nevertheless, the diminishment of the personal identity did not appear balanced by the development of a positive professional identity. The professional DR identity was perceived as unclear and confusing, and the adoption of an academic identity appeared to require DRs to have a greater degree of self-assurance or self-belief than was often the case [ 37 , 81 ].

Self as parasitic

Another change in identity manifested as DRs beginning to conceive of themselves as parasitic. DRs spoke of becoming ‘takers’, feeling that they were unable to provide or give anything to anyone. For some DRs, being ‘parasitic’ reflected them being on the bottom rung of the professional ladder or the ‘bottom of the pile’; thus, professionally only able to receive support and assistance rather than to provide for others. Other DRs reported more purposefully withdrawing from activities in which they were a ‘giver’, for example voluntary work, as providing or caring for others required time or energy that they no longer had [ 38 , 44 ]. Furthermore, DRs appeared to conceive of themselves as also causing difficulty or harm to others [ 81 ], as problems in relation to their PhD could lead them to unwillingly punishing close others, for example, through reducing the duration or quality of time spent together [ 38 ].

Feeling that close others were offering support appeared to heighten the awareness of the toll of the PhD on the individual and their close relationships, emphasising the huge undertaking and the often seemingly slow progress, and actually contributing to the sense of ambient guilt, shame, anger and failure [ 38 ]. Moreover, DRs spoke of feeling extreme guilt in perceiving that they had possibly sacrificed their own, and possibly family members’, current wellbeing and future financial security [ 49 ].

Death of self-agency

In addition to their sense of having to sacrifice their personal identity, DRs also expressed a loss of their sense of themselves as agentic beings. DRs expressed feeling powerless in various domains of their lives. First, DRs positioned the thesis as a powerful force able to overwhelm or swallow them [ 46 , 52 , 59 ]. Secondly, DRs expressed a sense of futility in trying to retain any sense of personal power in the climate of academia. An acute feeling of powerlessness especially in relation to supervisors was evident, with many examples provided of being treated as means to an end, as opposed to ends in themselves [ 39 , 42 , 62 ]. Supervisors did not interact with DRs in a holistic way that recognised their personhood and instead were perceived as prioritising their own will, or the will of other academics, above that of the DR [ 39 , 62 ].

Furthermore, DRs reported feeling as if they were used as a means for research production or furthering their supervisors’ reputations or careers [ 62 ]. DRs perceived that holding on to a sense of personal agency sometimes felt incompatible with having a positive supervisor relationship [ 42 ]. Thus whilst emotional distress, anger, disappointment, sadness, jealousy and resentment were clearly evident in relation to feeling excluded, used or over-powered by supervisors [ 37 , 42 , 52 , 62 ], DRs usually felt unable to change supervisor irrespective of how seriously this relationship had degraded [ 37 , 62 ]. Instead, DRs appeared to take on a position of resignation or defensive pessimism, in which they perceived their supervisors as thwarting their personhood, personal goals and preferences, but typically felt compelled to accept this as the status quo and focus on finishing their PhDs [ 42 ]. DRs resignation was such that they internalised this culture of silence and silenced themselves; tending to share litanies of problems with supervisors whilst prefacing or ending the statements with some contradictory or undermining phrase such as ‘but that’s okay’ [ 42 , 52 ].

The apparent lack of self-agency extended outward from the PhD into DRs not feeling able to curate positive life circumstances more generally [ 76 ]. A lack of time was perhaps the key struggle across both personal and professional domains, yet DRs paradoxically reported spending a lot of time procrastinating and rarely (if ever) mentioned time management as a necessary or desired coping strategy for the problem of having too little time [ 46 ]. The lack of self-agency was not only current but also felt in reference to a bleak and uncertain future; DRs lack of surety in a future in academia and the resultant sense of futility further undermined their motivation to engage currently with PhD tasks [ 38 , 40 ].

The system is sick

The higher-order theme ‘The system is sick’ represents systemic influences on DR mental health. First, ‘Most everyone’s mad here’ reflects the perceived ubiquity mental health problems amongst DRs. ‘Emperor’s new clothes’ reflects the DR experience of engaging in a performative piece in which they attempt to live in accordance with systemic rather than personal values. Finally, ‘Beware the invisible and visible walls’ reflects concerns with being caught between ephemeral but very real institutional divides.

Most everyone’s mad here

No studies focused explicitly on experiences of DRs who had been given diagnoses of mental health problems. Some study participants self-disclosed mental health problems and emphasised their pervasive impact [ 50 ]. Further lived experiences of mental distress in the absence of explicit disclosure were also clearly identifiable. The ‘typical’ presentation of DRs with respect to mental health appeared characterised as almost unanimous [ 39 ] accounts of chronic stress, anxiety and depression, emotional distress including frustration, anger and irritability, lack of mental and physical energy, somatic problems including appetite problems, headaches, physical pain, nausea and problems with drug and alcohol abuse [ 39 , 46 , 59 , 76 ]. Health anxiety, concerns regarding perceived new and unusual bodily sensations and perceived risks of developing stress-related illnesses were also common [ 46 , 59 , 76 ]. A PhD-specific numbness and hypervigilance was also reported, in which DRs might be less responsive to personal life stressors but develop an extreme sensitivity and reactivity to PhD-relevant stimuli [ 39 ].

An interplay of trait and state factors were suggested to underlie the perceived ubiquity of mental health problems amongst DRs. Etiological factors associated with undertaking a PhD specifically included the high workload, high academic standards, competing personal and professional demands, social isolation, poor resources in the university, poor living conditions and poverty, future and career uncertainty [ 36 , 41 , 43 , 46 , 49 , 76 ]. The ‘nexus’ of these factors was such that the PhD itself acted as a crucible; a process of such intensity that developing mental health problems was perhaps inevitable [ 39 ].

The perceived inevitability of mental health problems was such that DRs described people who did not experience mental health problems during a PhD as ‘lucky’ [ 39 ]. Supervisors and the wider academic system were seen to promote an expectation of suffering, for example, with academics reportedly normalising drug and alcohol problems and encouraging unhealthy working practices [ 39 ]. Furthermore, DRs felt that academics were uncaring with respect to the mental challenge of doing a PhD [ 39 ]. Nevertheless, academics were suggested to deny any culpability or accountability for mental health problems amongst DRs [ 39 , 59 , 74 ]. The cycle of indigenousness was further maintained by a lack of mental health literacy and issues with awareness, availability and access to help-seeking and treatment options amongst DRs and academics more widely [ 39 ]. Thus, DRs appeared to feel they were being let down by a system that was almost set up to cause mental distress, but within which there was a widespread denial of the size and scope of the problem and little effort put into identifying and providing solutions [ 39 , 59 ]. DRs ultimately felt that the systemic encouragement of unhealthy lifestyles in pursuit of academic success was tantamount to abuse [ 62 ].

A performance of optimum suffering

Against a backdrop of expected mental distress, DRs expressed their PhD as a performative piece. DRs first had to show just the right amount of struggle and difficulty; feeling that if they did not exhibit enough stress, distress and ill-health, their supervisors or the wider department might not believe they were taking their PhD seriously enough [ 40 ]. At the same time, DRs felt that their ‘researcher mettle’ was constantly being tested and they must rise to this challenge. This included first guarding against presenting oneself as intellectually inferior [ 36 ]. Yet it also seemed imperative not to show vulnerability more broadly [ 74 ]. Disclosing mental or physical health problems might lead not only to changed perceptions of the DR but to material disadvantage [ 74 ]. The poor response to mental health disclosures suggested to some DRs that universities might be purposefully trying to dissuade or discourage DRs with mental health problems or learning disabilities from continuing [ 74 ]. The performative piece is thus multi-layered, in that DRs must experience extreme internal psychological struggles, exhibit some lower-level signs of stress and fatigue for peer and faculty observance, yet avoid expressing any real academic or interpersonal weakness or the disclosure of any diagnosable disability or disease.

Emperor’s new clothes

DRs described feeling beholden to the prevailing culture in which it was expected to prioritise above all else developing into a competitive, self-promoting researcher in a high-performing research-active institution [ 39 , 42 ]. Supervisors often appeared the conduit for transmission of this academic ideal [ 74 ]. DRs felt reticent to act in any way which suggested that they did not personally value the pursuit of a leading research career above all else. For example, DRs felt that valuing teaching was non-conformist and could endanger their continuing success within their current institution [ 55 ]. Many DRs thus exhibited a sense of dissonance as their personal values often did not align with the institutional values they identified [ 74 ]. Yet DRs expressed a sense of powerlessness and a feeling of being ‘caught up’ in the values of the institution even when such values were personally incongruent [ 74 ]. The psychological toll of this sense of inauthenticity seemed high [ 55 ]. Where DRs acted in ways which ostensibly suggested values other than prioritising a research career, for example becoming pregnant, they sensed disapproval [ 76 ]. DRs also felt unable to challenge other ‘institutional myths’ for example, the perceived institutional denial of the duration of and financial struggle involved in completing a PhD [ 49 ]. There was a perceived tendency of academics to locate problems within DRs as opposed to acknowledging institutional or systemic inequalities [ 49 ]. DRs expressed strongly a sense in which there is inequity in support, resources and opportunities, yet universities were perceived as ignoring such inequity or labelling such divisions as based on meritocracy [ 36 , 74 ].

Beware the invisible and visible walls

DRs described the reality of working in academia as needing to negotiate a maze of invisible and visible walls. In the former case, ‘invisible walls’ reflect ephemeral norms and rules that govern academia. DRs felt that a big part of their continuing success rested upon being able to negotiate such rules [ 39 ]. Where rules were violated and explicit or implicit conflicts occurred, DRs were seen to be vulnerable to being caught in the ‘crossfire’ [ 36 ]. DRs identified academic groups and departments as being poor in explicitly identifying, discussing and resolving conflicts [ 37 ]. The intangibility of the ‘invisible walls’ gave rise to a sense of ambient anxiety about inadvertently transgressing norms and divides, such that some DRs reported behaving in ways that surprised even themselves [ 37 ].

Gendered and racial micropolitics of academic institutions were seen to manifest as more visible walls between people, with institutions privileging those with ‘insider’ status [ 36 ]. Women and people of colour typically felt excluded or disadvantaged in a myriad of observable and unobservable ways, with individuals able to experience both insider and outsider statuses simultaneously [ 36 , 37 ], for example when a male person of colour [ 36 ]. Female DRs suggested that not only must women prove themselves to a greater extent than men to receive equal access to resources, opportunities and acclaim but also are typically under additional pressure in both their professional and personal lives [ 37 , 52 , 76 ]. Women also felt that they had to take on more additional roles and responsibilities and encountered more conflicts in their personal lives compared to men [ 52 ]. Examples of professionally successful women in DRs’ departments were described as those who had crossed the divide and adopted a more traditionally male role [ 40 ]. Thus, being female or non-White were considered visible characteristics that would disadvantage people in the competitive academic environment and could give rise to a feeling of increased stress, pressure, role conflicts, and a feeling of being unsafe.

Seeing, being and becoming

The higher-order theme of ‘Seeing, being and becoming’ reflects protective and transformative influences on DR mental health. ‘De-programming’ refers to the DRs disentangling their personal beliefs and values from systemic values and also from their own tendency towards perfectionism. ‘The power of being seen’ reflects the positive impact on DR mental health afforded by feeling visible to personal and professional others. ‘Finding hope, meaning and authenticity’ refers to processes by which DRs can find or re-locate their own self-agency, purpose and re/establish a sense of living in accordance with their values. ‘The importance of multiple goals, roles and groups’ represents the beneficial aspects of accruing and sustaining multiple aspects to one’s identity and connections with others and activities outside the PhD. Finally, ‘The PhD as a process of transcendence’ reflects how the struggles involved in completing a PhD can be transformative and self-actualising.

De-programming

DRs reported being able to protect their mental health by ‘de-programming’ and disentangling their attitudes and practices from social and systemic values and norms. This disentangling helped negate DRs’ adopting unhealthy working practices and offered some protection against experiencing inauthenticity and dissonance between personal and systemic values.

First, DRs spoke of rejecting the belief that they should sacrifice or neglect personal relationships, outside interests and their self-identity in pursuit of academic achievement. DRs could opt-out entirely by choosing a ‘user-friendly’ programme [ 44 ] which encouraged balance between personal and professional goals, or else could psychologically reject the prevailing institutional discourse [ 40 ]. Rather than halting success, de-programming from the prioritisation of academia above all else was seen to be associated not only with reduced stress but greater confidence, career commitment and motivation [ 40 , 50 ]. It was also suggested possible to ‘de-programme’ in the sense of choosing not to be preoccupied by the ‘invisible walls’ of academia and psychologically ‘opt out’ of being concerned by potential conflicts, norms and rules governing academic workplace conduct [ 36 ]. Interaction with people outside of academia was seen to scaffold de-programming, by helping DRs to stay ‘grounded’ and offering a model what ‘normal’ life looks like. People outside of academia could also help DRs to see the truth by providing unbiased opinions regarding systemic practices [ 39 ].

A further way in which de-programming manifested was in DRs challenging their perfectionist beliefs. This include re-framing the goal as not trying to be the archetype of a perfect DR, and accepting that multiple demands placed on one individual invariably requires compromise [ 40 , 76 ]. DRs spoke of the need to conceptualise the PhD as a process, rather than just a product [ 46 , 82 ]. The process orientation facilitated framing of the PhD as just one-step in the broader process of becoming an academic as opposed to providing discrete evidence of worth [ 82 ]. Within this perspective, uncertainty itself could be conceived as a privilege [ 81 ]. The PhD was then seen as an opportunity rather than a test [ 37 , 46 ]. Moreover, the process orientation facilitated viewing the PhD as a means of growing into a contributing member of the research community, as opposed to needing to prove oneself to be accepted [ 82 ]. Remembering the temporary nature of the PhD was advised [ 45 ] as was holding on to a sense that not completing the PhD was also a viable life choice [ 76 ]. DRs also expressed, implicitly or explicitly, a decision to change their conceptualisation of themselves and their progress; choosing not to perceive themselves as stuck, but planning, learning and progressing [ 38 , 39 , 81 , 82 ]. This new perspective appeared to be helpful in reducing mental distress.

The power of being seen

DRs described powerful benefits to feeling seen by other people, including a sense of belonging and mattering, increased self-confidence and a sense of positive progress [ 37 ]. Being seen by others seems to provoke the genesis of an academic identity; it brings DRs into existence as academics. Being seen within the academic institution also supports mental health and can buffer emotional exhaustion [ 37 , 52 , 55 , 81 ]. DRs expressed a need to feel that supervisors, academics and peers were interested in them as people, their values, goals, struggles and successes; yet they also needed to feel that they and their research mattered and made a difference within and outside of the institution [ 42 , 52 , 81 ]. It was clear that DRs could find in their disciplinary communities the sense of belonging that often eluded them within their immediate departments [ 42 ]. Feeling a sense of belonging to the academic community seemed to buffer disengagement and amotivation during the PhD [ 81 ]. Positive engagement with the broader community was scaffolded by a sense of trust in the supervisor [ 81 ]. DRs often felt seen and supported by postdocs, especially where supervisors appeared absent or unsupportive [ 50 ].

Spending time with peers could be beneficial when there was a sense of shared experience and walking alongside each other [ 39 ]. Friendship was seen to buffer stress and protect against mental health problems through the provision of social and emotional support and help in identifying struggles [ 39 , 43 ]. In addition to relational aspects, the provision of designated physical spaces on campus or in university buildings also seemed important to being seen [ 37 ]. Peers in the university could provide DRs with further physical embodiments of being seen, for example, gift-giving in response to their birthdays or returning from leave [ 37 , 50 ]. Outside of the academic institution, DRs described how being seen by close others could support DRs to be their authentic selves, providing an antidote to the invisible walls of academia [ 50 ]. Good quality friendships within or outside academia could be life-changing, providing a visceral sense of connection, belonging and authenticity that can scaffold positive mental health outcomes during the PhD [ 39 ]. Pets could also serve to help DRs feel seen but without needing to extend too much energy into maintaining social relationships [ 50 ].

Finally, DRs also needed to see themselves, i.e. to begin to see themselves as burgeoning academics as opposed to ‘just students’ [ 81 ]. Feeling that the supervisor and broader academic community were supportive, developing one’s own network of process collaborators and successfully obtaining grant funding seemed tangible markers that helped DRs to see themselves as academics [ 37 , 81 ]. Seeing their own work published was also helpful in providing a boost in confidence and being a joyful experience [ 42 ]. Moreover, with sufficient self-agency, DRs can not only see themselves but render themselves visible to other people [ 37 ].

Multiple goals, roles and groups

In antidote to the diminished personal identity and enmeshment with the PhD, DRs benefitted from accruing and sustaining multiple goals, roles, occupations, activities and social group memberships. Although ‘costly’ in terms of increased stress and role conflicts, sustaining multiple roles and activities appeared essential for protecting against mental health problems [ 50 , 68 ].

Leisure activities appeared to support mental health through promoting physical health, buffering stress, providing an uplift to DRs’ mood and through the provision of another identity other than as an academic [ 44 , 50 , 76 ]. Furthermore, engagement in activities helped DRs to find a sense of freedom, allowing them to carve up leisure and work time and psychologically detach from their PhD [ 68 , 76 ]. Competing roles, especially family, forced DRs to distance themselves from the PhD physically which reinforced psychological separation [ 50 , 59 ]. Engaging in self-care and enjoyable activities provided a sense of balance and normalcy [ 39 , 44 , 68 ]. This normalcy was a needed antidote to abnormal pressure [ 59 ]. Even in the absence of fiercely competing roles and priorities, DRs still appeared to benefit from treating their PhD as if it is only one aspect of life [ 59 ]. Additional roles and activities reduced enmeshment with the PhD to the extent that considering not completing the PhD was less averse [ 40 ]. This position appeared to help DRs to be less overwhelmed and less sensitive to perceived and anticipated failures.

Finding hope, meaning and authenticity

Finding hopefulness and meaning within the PhD can scaffold a sense of living a purposeful, enjoyable, important and authentic life. Hopefulness is predicated on the ability to identify a goal, i.e. to visualise and focus on the desired outcome and to experience both self-agency and potential pathways towards the goal. Hopefulness was enhanced by the ability to break down tasks into smaller goals and progress in to ‘baby steps’ [ 38 , 59 ]. In addition, DRs benefitted from finding explicit milestones against which they can compare their progress [ 59 ], as this appeared to feed back into the cycle of hopeful thinking and spur further self-agency and goal pursuit.

The experience of meaning manifested in two main ways; first as the more immediate lived experience of passion in action [ 76 ]. Secondly, DRs found meaning in feeling that in their PhD and lives more broadly they were living in accordance with their values, for example, experiencing their own commitment in action through continuing to work on their PhD even when it was difficult to do so [ 76 ]. DRs who were able to locate their PhD within a broader sense of purpose appeared to derive wellbeing benefits. There was a need to ensure that values were in alignment, for example, finding homeostasis between emotional, intellectual, social and spiritual parts of the self [ 46 , 59 , 90 ].

The processes of finding hopefulness and meaning appear to be largely relational. Frequent contact with supervisors in person and social and academic contact with other DRs were basic scaffolds for hope and meaning [ 52 ]. DRs spoke of how a sense that their supervisors believed in them inspired their self-agency and motivation [ 42 , 62 , 76 ]. Partners, friends and family could also inspire motivation for continuing in PhD tasks [ 44 , 76 ]. Other people also could help instil a sense of motivation to progress and complete the PhD; a sense of being seen is to be beholden to finish [ 39 ]. Meaning appeared to be scaffolded by a sense of contribution, belonging and mattering [ 81 ] and could arise from the perception of putting something into the collective pot, inspiring hopefulness and helping others [ 39 , 42 ]. Moreover, hopefulness, meaning and authenticity also appeared mutually reinforcing [ 81 ]. Finding meaning and working on a project which is in accordance with personal values, preferences and interests is also helpful in completing the PhD and provides a feedback loop into hope, motivation and agentic thinking [ 39 , 81 ]. Furthermore, DRs could use agentic action to source a community of people who share their values, enabling them to engage in collective authenticity [ 39 ].

The PhD as a process of transcendence

The immense challenge of the PhD could be a catalyst for growth, change and self-actualisation, involving empowerment through knowledge, self-discovery, and developing increased confidence, maturity, capacity for self-direction and use of one’s own autonomy [ 44 , 82 ]. The PhD acted as a forge in which DRs were tested and became remoulded into something greater than they had been before [ 44 , 82 , 90 ]. The struggles endured during the PhD caused DRs to reconsider their sense of their own capacities, believing themselves to be more able than they previously would have thought [ 50 ]. The struggles endured added to the sense of accomplishment. A trusted and trusting supervisor appears to aid in the PhD being a process of transcendence [ 62 ].

More broadly, the PhD also helped DRs to transcend personal tragedy, allowing immersion in a meaningful activity which begins as a means of coping and becomes something completely [ 39 ]. The PhD could also serve as a transformative selection process for DRs’ social relationships, with some relationships cast aside and yet others formed anew [ 39 ]. Overall, therefore, the very aspects of the PhD which were challenging, and distressing could allow DRs to transcend their former selves and, through the struggle, become something more.

Summation of results

The findings regarding the risk and protective factors associated with DR mental health have been summarised in Table 3 in relation to (1) the type of research evidencing the factor (i.e. whether the evidence is quantitative only, part of the meta-synthesis only, or evident in both results sections); and (2) the volume of evidence (i.e. whether the factor was found in a single study or across multiple studies). The factors in the far-right column (i.e. the factors found across multiple research studies utilising both qualitative and quantitative methods) are the ones with the strongest evidence at present.

This systematic review summarises a heterogeneous research area, with the aim of understanding the mental health of DRs, including possible risk and protective factors. The qualitative and quantitative findings presented here suggest that poor mental health is a pertinent problem facing DRs; stress appears to be a key issue and significantly in excess of that experienced in the general population. Several risk and protective factors at the individual, interpersonal and systemic levels emerged as being important in determining the mental health of DRs. The factors with the strongest evidence-base (i.e. those supported by multiple studies using qualitative and quantitative findings) denote that being female and isolated increases the risk of the mental health problems, whereas seeing the PhD as a process, feeling socially supported, having a positive supervisor relationship and engaging in self-care is protective.

Results in context

Stress can be defined as (1) the extent to which a stimulus exerts pressure on an individual, and their propensity to bear the load; (2) the duration of the response to an aversive stimuli, from initial alert to exhaustion; or (3) a dynamic (im)balance between the demands and personal resource to manage those demands [ 91 ]. The Perceived Stress Scale (PSS) [ 18 , 19 ] used in our meta-analysis is aligned with the third of these definitions. As elaborated upon within the Transactional Model of Stress [ 92 ], stress is conceptualised as a persons’ appraisal of the internal and external demands put upon them, and whether these exceed their available resources. Thus, our results suggest that, when compared to the general population, PhD students experience a greater maladaptive imbalance between their available resources and the demands placed upon them. Stress in itself is not a diagnosable mental health problem, yet chronic stress is a common precipitant to mental health difficulties such as depression and posttraumatic stress disorder [ 93 , 94 ]. Therefore, interventions should seek to bolster DRs’ resources and limit demands placed on them to minimise the risks associated with acute stress and limit its chronicity.

Individual factors

Female DRs were identified as being at particular risk of developing mental health difficulties. This may result from additional hurdles when studying in a male-dominated profession [ 95 , 96 , 97 ], and the expectation that in addition to their doctoral studies, females should retain sole or majority responsibility for the domestic and/or caring duties within their family [ 52 , 76 ]. It may also be that females are more willing to disclose and seek help for mental health difficulties [ 98 ]. Nevertheless, the World Health Organisation (WHO) mental health survey results indicate that whilst anxiety and mood disorders are more prevalent amongst females, externalising disorders are more common in males [ 99 ]. As the vast majority of studies in this review focussed on internalising problems (e.g. stress, anxiety and depression) [ 37 , 64 , 79 , 80 , 83 , 89 ], this may explain the gender differences found in this review. Further research is needed to explore which perspective, if any, may explain gender gap in our results.

Perhaps unsurprisingly, self-care was associated with reduced mental health problems. The quantitative findings suggest that all types of self-care are likely to be protective of mental health (i.e. physical, emotional, professional and spiritual self-care). Self-care affords DRs the opportunity to take time away from their studies and nurture their non-PhD identities. However, the results from our meta-synthesis suggest that DRs are not attending to their most basic needs much less engaging in self-care behaviours that correspond to psychological and/or self-fulfilment needs [ 100 ]. Consequently, an important area for future enquiry will be identifying the barriers preventing DRs from engaging in self-care.

Interpersonal factors

Across both quantitative and qualitative studies, interpersonal factors emerged as the most salient correlate of DR mental health. That is, isolation was a risk factor, whereas connectedness to others was a protective factor. There was some variability in how these constructs were conceptualised across studies, i.e. (1) isolation: a lack of social support, having fewer social connections, feeling isolated or being physically separate from others; and (2) social connectedness: multiple group membership, academic relationships or non-academic relationships; but there was no indication that effects varied between concepts. The relationship between isolation and negative health consequences is well-established, for example both physical and mental health problems [ 101 ], and even increased mortality [ 102 ]. Conversely, social support is associated with reduced stress in the workplace [ 103 , 104 ]. Reducing isolation is therefore a promising interventional target for improving DRs’ mental health.

The findings regarding isolation are even more alarming when considered alongside the findings from several studies that PhD studies are consistently reported to dominate the lives of DRs, resulting in poor ‘work-life balance’ and losing non-PhD aspects of their identities. The negative impact of having fewer identities [ 105 ] can be mitigated by having a strong support network [ 106 ], and increasing multiple group memberships [ 107 ]. But for DRs, it is perhaps the absence of this social support, combined with identity impoverishment, which can explain the higher than average prevalence of stress found in our meta-analysis.

Systemic factors

DRs’ attitudes towards their studies may be a product of top-down systemic issues in academia more broadly. Experiencing mental health problems was reported as being the ‘norm’, but also appeared to be understood as a sign of weakness. The meta-synthesis results suggest that DRs believed their respective universities prioritise academic success over workplace wellbeing and encourage unhealthy working habits. Working in an unsupportive and pressured environment is strongly associated with negative psychological outcomes, including increased depression, anxiety and burnout [ 108 ]. The supervisory relationship appeared a particularly important aspect of the workplace environment. The quantitative analysis found a negative correlation between inspirational supervision and mental health problems. Meta-synthetic finding suggested toxic DR-supervisor relationships characterised by powerlessness and neglect, as well as relationships where DRs felt valued and respected—the former of these being associated with poor mental health, and the latter being protective. The association between DR-supervisor relationship characteristics needs to be verified using quantitative methods. Furthermore, DRs’ sense that they needed to exhibit ‘optimum suffering’, which appears to reflect a PhD-specific aspect of a broader academic performativity [ 109 ], is an important area for consideration. An accepted narrative around DRs needing to experience a certain level of dis/stress would likely contribute to poor mental health and as an impediment to the uptake and effectiveness of proffered interventions. Although further research is needed, it is apparent that individual interventions alone are not sufficient to improve DR mental health, and that a widespread culture shift is needed in order to prevent the transmission of unhealthy work attitudes and practices.

Limitations of the literature

Although we found a respectable number of articles in this area, the focus and measures used varied to the extent that typical review analysis procedures could not be used. That is, there was much heterogeneity in terms of how mental health was conceptualised and measured, as well as the range of risk and protective factors explored. Similarly, the quality of the studies was hugely variable. Common flaws amongst the literature include small sample sizes, the use of unvalidated tools and the incomplete reporting of results. Furthermore, for qualitative studies specifically, there appeared to be a focus on breadth instead of depth, particularly in relation to studies using mixed methods.

The generalisability of our findings is limited largely due to the lack of research conducted outside of the US, but also because we limited our review to papers written in English only. The nature of doctoral studies varies in important ways between studies. For example, in Europe, PhD studies usually apply for funding to complete their thesis within 3–4 years and must know their topic of interest at the application stage. Whereas in the US, PhD studies usually take between 5 and 6 years, involve taking classes and completing assignments, and the thesis topic evolves over the course of the PhD. These factors, as well as any differences in the academic culture, are likely to affect the prevalence of mental health problems amongst DRs and the associated risk and protective factors. More research is needed outside of the US.

‘Mental health’ in this review was largely conceptualised as a type of general wellbeing rather than a clinically meaningful construct. None of the studies were ostensibly focused on sampling DRs who were currently experiencing or had previously experienced mental health problems per se, meaning the relevance of the risk, vulnerability and protective factors identified in the meta-synthesis may be more limited in this group. Few studies used clinically meaningful measures. Where clinical measures were used, they captured data on common mental health problems only (i.e. anxiety and depression). Due to these limitations, we are unable to make any assertions about the prevalence of clinical-level mental health problems amongst DRs.

Limitations of this review

As a result of the heterogeneity in this research area, some of the results presented within this review are based on single studies (e.g. correlation data; see Fig. 5 ) rather than the amalgamation of several studies (e.g. meta-analysis/synthesis). To aid clarity when interpreting the results of this review, we have (Table 3 ) summarised the volume of evidence supporting risk and protective factors. Moreover, due to the small number of studies eligible for inclusion in this review, we were unable to test whether any of our findings are related to the study characteristics (e.g. year of publication, country of origin, methodology).

We were able to conduct three meta-analyses, one of which aimed to calculate the between-group effect size on the PSS [ 18 , 19 ] between DRs and normative population data. Comparing these data allowed us to draw some initial conclusions about the prevalence of stress amongst DRs, yet we were unable to control for other group differences which might moderate stress levels. For example, the population data was from people in the United States (US) in 1 year, whereas the DR data was multi-national at a variety of time points; and self-reported stress levels may vary with nationality [ 110 ] or by generation [ 111 , 112 ]. Moreover, two of the three meta-analyses showed significant heterogeneity. This heterogeneity could be explained by differences in the sample characteristics (e.g. demographics, country of origin), doctoral programme characteristics (e.g. area of study, funding status, duration of course) or research characteristics (e.g. study design, questionnaires used). However, due to the small number of studies included in these meta-analyses, we were unable to test any of these hypotheses and are therefore unable to determine the cause of this heterogeneity. As more research is conducted on the mental health of DRs, we will be able to conduct larger and more robust meta-analyses that have sufficient power and variance to statistically explore the causes of this heterogeneity. At present, our findings should be interpreted in light of this limitation.

Practice recommendations

Although further research is clearly needed, we assert that this review has identified sufficient evidence in support of several risk and protective factors to the extent that they could inform prevention and intervention strategies. Several studies have evidenced that isolation is toxic for DRs, and that social support can protect against poor mental health. Initiatives that provide DRs with the opportunity to network and socialise both in and outside of their studies are likely to be beneficial. Moreover, there is support for psychoeducation programmes that introduce DRs to a variety of self-care strategies, allow them to find the strategies that work for them and encourage DRs to make time to regularly enact their chosen strategies. Finally, the supervisory relationship was identified as an important correlate of DR mental health. Positive supervision was characterised as inspirational and inclusive, whereas negative supervision productised DRs or neglected them altogether. Supervisor training programmes should be reviewed in light of these findings to inform how institutions shape supervisory practices. Moreover, the initial findings reported here evidence a culture of normalising and even celebrating suffering in academia. It is imperative therefore that efforts to improve and protect the mental health of DRs are endorsed by the whole institution.

Research recommendations

First, we encourage further large-scale mental health prevalence studies that include a non-PhD comparison group and use validated clinical tools. None of the existing studies focused on the presence of serious mental health problems—this should be a priority for future studies in this area. Mixed-methods explorations of the experiences of DRs who have mental health problems, including serious problems, and in accessing mental health support services would be a welcome addition to the literature. More qualitative studies involving in-depth data collection, for example interview and focus group techniques, would be useful in further supplementing findings from qualitative surveys. Our review highlights a need for better communication and collaboration amongst researchers in this field with the goal of creating a level of consistency across studies to strengthen any future reviews on this subject.

The results from this systematic review, meta-analysis and meta-synthesis suggest that DRs reported greater levels of stress than the general population. Research regarding the risk and protective factors associated with the mental health of DRs is heterogenous and disparate. Based on available evidence, robust risk factors appear to include being isolated and being female, and robust protective factors include social support, viewing the PhD as a process, a positive DR-supervisor relationship and engaging in self-care. Further high-quality, controlled research is needed before any firm statements can be made regarding the prevalence of clinically relevant mental health problems in this population.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Confidence intervals

Doctoral researchers

Higher Education Statistics Agency

Perceived Stress Scale

Standard deviation

United Kingdom

United States

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Acknowledgements

Thank you to the Office for Students for their funding to support this work; and thank you to the University of Sussex Doctoral school and our steering group for championing and guiding the ‘Understanding the mental health of Doctoral Researchers (U-DOC)’ project.

The present project was supported by the Office for Students Catalyst Award. The funder had no involvement in the design of the study, the collection, analysis or interpretation of the data, nor the writing of this manuscript.

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CH contributed to the conceptualisation, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, supervision, validation, visualisation, writing—original draft preparation and writing—review and editing of this paper. LC contributed to the data curation, investigation, project administration, validation and writing—review and editing of this paper. SV contributed to the data curation, formal analysis, investigation, project administration, validation and writing—review and editing of this paper. PR contributed to the funding acquisition, project administration, supervision and writing—review and editing of this paper. JN contributed to the conceptualisation, funding acquisition, methodology, project administration, supervision, validation, writing—original draft preparation and writing—review and editing of this paper. CB contributed to the conceptualisation, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, supervision, validation, visualisation, writing—original draft preparation and writing—review and editing of this paper. The author(s) read and approved the final manuscript.

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Hazell, C.M., Chapman, L., Valeix, S.F. et al. Understanding the mental health of doctoral researchers: a mixed methods systematic review with meta-analysis and meta-synthesis. Syst Rev 9 , 197 (2020). https://doi.org/10.1186/s13643-020-01443-1

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Barriers to help-seeking, accessing and providing mental health support for medical students: a mixed methods study using the candidacy framework

• Elena Sheldon   ORCID: orcid.org/0000-0002-1927-471X 1 , 4 ,
• Naseeb Ezaydi   ORCID: orcid.org/0000-0002-2642-1108 1 ,
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The mental health of medical students is a national and international problem increasing in both demand and acuity. Medical students face barriers to accessing mental health support that is clinically effective, timely and appropriate for their needs. This mixed methods study aimed to explore experiences of these barriers and the challenges to health service delivery aligned to the Candidacy Framework.

One hundred three medical students studying at The University of Sheffield completed an online survey comprising the CCAPS-34 and follow-up questions about service access and use. Semi-structured interviews with a nested sample of 20 medical students and 10 healthcare professionals explored barriers to service access and provision. A stakeholder panel of medical students and professionals met quarterly to co-produce research materials, interpret research data and identify touchpoints by pinpointing specific areas and moments of interaction between a medical student as a service user and a mental health service.

Medical students who experienced barriers to help-seeking and accessing support scored significantly higher for psychological symptoms on the CCAPS-34. Uncertainty and fear of fitness to practice processes were important barriers present across all seven stages of candidacy. The fragmented structure of local services, along with individual factors such as perceived stigma and confidentiality concerns, limited the progression of medical students through the Candidacy Framework (a framework for understanding the different stages of a person’s journey to healthcare).

This study outlines important areas of consideration for mental health service provision and policy development to improve access to and the quality of care for medical students.

Peer Review reports

The mental health of medical students is a national and international problem [ 1 ], requiring urgent attention [ 2 ]. Mental health problems can emerge as early as the first year with symptoms of depression, anxiety, burnout and suicidal ideation [ 3 , 4 ]. A meta-analysis of 183 studies across 43 countries showed that the prevalence of depression among medical students was 27%, with 11% of those students reporting suicidal ideation and more than 80% feeling under-supported [ 1 ]. Episodes of poor mental health are associated with adverse outcomes such as alcohol and substance abuse, self-harm and dropping out of medical school [ 3 , 5 ].

Medical students face particular sets of barriers to help-seeking and accessing mental health support; less than a quarter of those with clinical levels of depression report using counselling services [ 6 ]. Barriers include stigma, perceiving a mental health problem as a weakness and beliefs about “fitness to practice” (FTP) proceedings, with presumed implications for career progression [ 7 ] and the possibility of expulsion [ 8 ]. Jadzinski et al. [ 9 ] reported a lack of understanding of what FTP expectations are for medical students and inconsistencies with Higher Education Institution (HEI) processes in managing FTP concerns. Internationally, the barriers to help-seeking, which affect medical students disproportionately, are complex and multi-faceted [ 10 , 11 ].

HEIs have seen a growing demand for services to meet the mental health needs of medical students [ 12 , 13 , 14 ]. University support services are required to provide brief in-house support to students, including counselling or mental health centres, disability support, and wellbeing services. Longer-term or specialist support for acute mental health problems are provided by external services. The Student Services Partnership Evaluation and Quality Standards (SPEQS), developed by Sheffield and University College London, included a toolkit addressing some of the challenges to cross-sector working from a professional perspective [ 15 ]. SPEQS provides a generic groundwork that must now be tailored to understanding how professionals can better meet the specific mental health needs of medical students and the associated challenges.

Access barriers, difficulty navigating pathways and overstretched health services mean that medical students who feel able to seek help can fall between the gaps [ 16 ]. Medical students may delay approaching services until their needs are severe or impact their studies [ 17 ], and may turn to more acute care settings to access professional support [ 18 ]. Understanding the experiences of medical students who have ‘fallen through the cracks’ and the challenges to treatment access are essential to improving the quality of services [ 19 ]. The aim of this study was to examine how barriers to accessing and navigating mental health services arise and intersect with challenges to service provision in the unique context of medical student mental health.

Theoretical framework

We adopted the Candidacy Framework developed by Mary Dixon-Woods and colleagues [ 20 ]. Candidacy represents the idea that an individual’s access to and successful use of health services is an iterative process influenced by individual, professional, organisational, structural and resource factors. It has been used to understand healthcare experiences of vulnerable groups, including persons with MS [ 21 ] and young onset dementia [ 22 ], but has not been applied to medical students who experience mental health problems. Our study explored help-seeking behaviours, access barriers and the challenges to health service delivery aligned to the Candidacy Framework.

This study used a mixed methods sequential design, consisting of two distinct work packages: (1) quantitative survey to describe patterns of help seeking and unmet mental health needs and (2) nested semi-structured interviews to understand more nuanced aspects of accessing and delivering support. We adhered to the Good Reporting of A Mixed Methods Study (GRAMMS) guidelines (Additional Material 1) [ 19 , 20 ].

Improved systems of support can only be achieved in partnership with their intended users, participating on equal terms as stakeholders [ 23 ]. Based on the reported benefits of service user involvement in mental health service development and delivery [ 24 ], a stakeholder panel of nine medical students and five professionals met quarterly to co-produce research materials, interpret research data and identify touchpoints by pinpointing specific moments and areas of interaction between a medical student as a service user and a mental health service. These touchpoints are critical for understanding the user experience and are often targets for improving satisfaction and effectiveness. Professionals were selected for involvement in the stakeholder panel based on their organisation and role. Staff from the University of Sheffield’s Medical School ( n  = 2), NHS professionals working in community mental health settings (including low-intensity and acute care provision) ( n  = 2), and a researcher specialising in the field of student mental health ( n  = 1) were approached by e-mail. Medical students with lived experience were self-selected following an advertisement that was circulated by e-mail to all medical students at The University of Sheffield’s Medical School. The stakeholder panel therefore involved a diversity of voices to ensure meaningful input throughout that was based on both professional and lived experiences.

Work Package 1 involved a cross-sectional online survey of medical students studying at School of Medicine and Population Health, The University of Sheffield. The survey included the Counselling Centre Assessment of Psychological Symptoms (CCAPS-34) [ 25 ], a 34-item instrument with seven distinct sub-scales related to psychological symptoms and distress in university students. Items are rated on a five-point Likert scale (0 = not at all like me, 4 = extremely like me) with higher scores indicating higher severity. The survey employed multiple choice questions on participant demographics, help-seeking behaviours and service use (Additional Material 2). The survey was conducted using the Qualtrics Research Suite (Qualtrics, Provo, UT), with a one-week response window from 04/11/2022 to 11/11/2022.

An email invitation was sent to all eligible medical students aged 18 or over and studying MBChB Medicine (A100) degree or MBChB Graduate Entry Medicine (A101) at The University of Sheffield. The email included a webpage link to the Participant Information Sheet and online survey. Informed consent was completed online prior to data collection. The survey link was advertised on the student intranet news feed.

To ensure confidentiality, names were not collected except where medical students consented to contact for the interviews. Data was stored on a secure file server accessible only to the research team. Descriptive statistics and one-way ANOVAs were produced using the software R version 4.2.1 to explore differences in symptom profiles between demographics, help-seeking behaviours and service use. CCAPS-34 subscales could not be calculated where participants responded with the same value for each question in the subscale. At least 33% of questions must be answered in the subscale to calculate a valid subscale score. The overall CCPAS-34 scores and subscale scores are calculated by the mean of the available items, assuming the missing data rules hold. Details on the scoring and handling of missing data for the CCPAS-34 can be found in Additional Material 3.

Medical students responding to the survey were invited to register interest in semi-structured interviews (Work Package 2). The survey therefore provided a nested cohort from which a purposive sample of medical students were approached by email. Sampling was based on those with the highest CCAPS-34 scores, or a disclosure of previous or current use of mental health services. Medical students who disclosed mental health concerns but decided not to seek help based on their responses to the multiple-choice questions on help-seeking behaviours and service use were also approached. The stakeholder panel informed sampling based on maximum variation for demographic characteristics. Professionals were contacted for interview by email based on their organisation and role. 20 medical students and ten professionals were invited to take part by e-mail that provided a Participant Information Sheet and contact details for the research team This was considered adequate for data saturation [ 26 ] using established frameworks [ 27 ] and demonstrates integration of mixed methods at the design stage.

Interviews took place using a secure internet application with an audio consent procedure. Topic guides were co-designed with the stakeholder panel (Additional Materials 4 and 5). Potential items for the topic guide were informed by theories of (non-) help-seeking in young adults [ 28 ], covering known barriers to help-seeking and risk factors. Stakeholders selected, modified and added items for inclusion in the topic guide based on their lived experiences, values and priorities. Final drafts of the topic guides were reviewed and approved by the stakeholder panel. Encrypted digital recordings were transcribed verbatim. Two researchers analysed the transcripts and all free-text survey responses within NVivo Version 12 (QSR International), using the five stages of National Centre for Social Care ‘Framework’ analysis approach: familiarisation; identifying themes; indexing; charting; interpretation and mapping [ 29 ]. This process involved using codes as a system for marking ‘parts of the text that are of special interest’ and themes as converting ‘codes into core concepts that represent the most important aspects of the results’ [ 30 ] based on the Candidacy Framework (Table  1 ).

Ethical considerations

This project received favourable opinion from ScHARR Research Ethics Committee (049592).

Quantitative findings

Survey demographics.

We received 103 survey responses (103/1500, 6.9% response rate). Table 2 shows a breakdown of participant demographic categories and responses to the follow-up questions. The majority of medical students were female (66.0%), white (69.9%) and studying in their home/birth country (93.2%). Most respondents were in their first year of study (14.6%) with fewer respondents in their fifth (10.7%) and sixth years (7.8%).

CCPAS-34 scores

Of the 103 respondents, 102 completed all CCAPS items; the remaining participant completed less than 50% so were excluded in the analysis. The mean (SD) overall score for the 102 participants was 1.28 (0.62). Medical students obtained the highest score on Social Anxiety (mean = 1.96, SD = 0.94) and the lowest score on Frustration/Anger (mean = 0.76, SD = 0.68). The following subscales could not be scored because those participants responded with the same value for each question in that subscale, so their score could not be calculated: Academic Distress (8/103), Alcohol (39/103), Depression (12/103), Eating Concerns (49/103), Frustration (36/103), Generalised Anxiety (6/103) and Social Anxiety (6/103).

Statistical findings

The results of the statistical analyses are found in Additional Material 6. No significant findings were found between overall CCAPS-34 scores and participant demographics or the subscale scores and demographics ( p  > 0.05). Significant responses were found between overall CCAPS-34 scores and the follow-up questions, indicating those who responded ‘yes’ to those questions scored significantly higher for psychological symptoms ( p  < 0.05).

Significant responses were found between the following CCAPS-34 subscale scores and follow-up questions: Academic Distress, Depression, Frustration, Generalised Anxiety and Social Anxiety (where three out of the four questions were significant). Floor and ceiling effects for each subscale were calculated with the unadjusted mean differences (Additional Material 7).

Qualitative findings

Of the 103 respondents, 64 (62%) medical students consented to be contacted for interview. Interviews were conducted with 20 medical students and 10 professionals (see Table  3 for participant characteristics). As well as generic issues with access to mental healthcare for all University students, medical students face particular barriers at each stage of the Candidacy Framework (Fig.  1 ). Uncertainty and fear of FTP processes were mapped to all stages of candidacy as an important barrier to help-seeking and accessing support. The stigma of appearing “weak” in medical school culture; the challenges of clinical placements; and confidentiality concerns when working clinically were also highlighted as key individual-level barriers. Healthcare professionals offered insights into the fragmented structure of local services, in particular the gap in support provision between primary and secondary care.

The Candidacy Framework aligned with key barriers and facilitators in the medical student pathway

Fitness to practice concerns across the stages of candidacy

FTP concerns were mapped as a barrier across all Candidacy stages. Medical students who were unable to progress beyond Stage 1 reported being in denial about having a mental health problem in fear of FTP ramifications: ‘As soon as you access support you’re admitting you have a problem, so if you don’t access the support it’s just like, the denial can keep going’ (S14). Some medical students rejected referrals and offers of treatment (Stage 6): ‘I was really worried it was going to have an effect on whether or not people thought I was able to study, so I didn’t access it’ (S01).

Professionals described how FTP concerns limited data sharing opportunities where medical students chose to opt out of their information being shared with the University. In reality however, FTP proceedings were experienced as exceptional cases where symptoms were complex, acute and presented significant risks to patient safety. Crucially, professionals emphasised that concerns arise when care is not sought or accepted: ‘We do our best to say to students you’re not going to get into trouble for having a health problem. […]. You will get into trouble for letting it get worse and not dealing with it, because you’ve got a responsibility to be safe and practice’ (P03).

Stage 1: Identification of candidacy

Medical students spend a considerable amount of time in this first stage of the Candidacy Framework where they determine whether their mental health concern warrants professional support. One reason for failing to identify themselves as suitable candidates was the prioritisation of studies over mental health and wellbeing. The stigma associated with mental illness amongst peers meant that seeking help is perceived as weakness or professional risk.

‘For people who work in the medical field or want to, then I think [the stigma] can be higher because they think it shouldn’t happen to them and they shouldn’t need support’ (P06).

Stage 2: Navigation of services

Once medical students identified themselves as a candidate, they determined where and how to receive appropriate care. Participants described:

A lack of knowledge of services available for their mental health concern

Practical barriers related to studying medicine, for example inconvenient appointment times. Often participants described that they ‘just simply don’t have the time’ (S16).

‘That’s definitely one of the reasons why I wouldn’t access help. [...] It’s really hard to arrange appointments if you don’t know who or what you’re going to be doing’ (S18).

Professionals reflected that whilst there are a range of services accessible to medical students, they often lack awareness of what support is available. The challenge for professionals is to help medical students navigate that system by signposting to the appropriate service, arranging access via referrals or providing information on how to access a service.

‘I think being able to work out which box you fit into and how to access that is more of a challenge than there not being support around.’ (P02).

Stage 3: Permeability of services

Most medical students reported that services were chosen based on ease of access or permeability. For those with common or ‘mild’ mental health symptoms, the University services and NHS Sheffield Talking Therapies were accessed often and easily. Medical students with acute or complex mental health problems defaulted to services that were most permeable – i.e., those with the fewest criteria to gain entry, such as the emergency department. Mental health services that would be appropriate for their level of acuity were considered inaccessible due to long waiting lists and complex referral processes.

‘There’s a lot available for mild mental health, but for the more complex or unwell states of mental health it’s more difficult. […] It’s difficult when you fall somewhere between mild mental health and severe mental health’ (S02).

Professionals recognised the gap between care offered at a primary and secondary care level. This important challenge to service provision arose when medical students required longer-term or specialist services which have high thresholds for acceptance. Professionals reflected however, that this barrier is experienced beyond the medical student context and is recognised nationally in the UK.

‘There’s a big gap between what’s available at the primary and secondary care level. So people with acute mental health problems where a short-term approach is not going to be helpful for, it’s hard to access psychotherapy for those people’ (P10).

Stage 4: Appearances at healthcare

Medical students expressed feeling uncomfortable attending appointments to discuss their mental health concerns to practitioners and described concerns that they might be known to them in an academic context.

‘When I go to the GP, anything that I’m saying, I’m potentially saying to a future colleague that I’m potentially working with so how are they going to view me?’ (S07).

Stage 5: Adjudication by professionals

Medical students then faced the task of convincing healthcare providers of their candidacy for care. Many participants reported feeling invalidated or dismissed due to preconceptions held on medical students’ risk.

Healthcare provider adjudications were influenced by perceptions that there is nothing suitable to offer students and were therefore considered unfit candidates for care. In these cases, medical students reported being discharged without support or being signposted to an alternative service with lower thresholds for acceptance.

‘They went along the lines of you’re a medical student, you’re functioning, you don’t need input from us. So they discharged me, and they discharged me without any support’ (S01).

Professionals described their frustrations however, when support cannot be offered based on a medical students’ presentation and the level of acuity required for acceptance. What was perceived as dismissive by medical students may reflect limited support options at a secondary care level.

‘So often you want to give people something or you can identify something that could really help them but they don’t meet the criteria to access that. So that’s a big barrier’ (P01).

Stage 6: Offers of services

Medical students rejected offers of care due to:

Practical barriers, such as long working hours on placement.

Inappropriate or limited support offered that did not fit their needs.

Support not offered within an acceptable time frame.

‘During that time I was on placement and I was like, they’d already wriggled around my placement, I really I can’t do that again. So I declined that’ (S06).

Some medical students accepted care offers from private services to overcome these barriers. This is particularly unacceptable when considering the widening participation strategies to include medical students who are less likely to have affluent socioeconomic backgrounds and experience increased financial burden whilst studying in the UK. Professionals made efforts to overcome barriers by adapting to individual needs, for example by offering study leave so that treatment offers could be facilitated.

Stage 7: Operating conditions

Participants described overarching influences, including:

Poor coordination, continuity or transference of care, especially for those who received care prior to university.

Low capacity due to high caseloads and demand for local services.

Limited room space; inappropriate waiting environments.

‘I think due to waiting time, if you are at the point where you’re trying to access services and they’re just not there, it deters you from it.’ (S17).

Summary of principal findings

Medical students who experienced higher levels of psychological symptoms were significantly more likely to report help-seeking concerns. This study presents key barriers to accessing mental health support at each stage of the Candidacy Framework. Uncertainty and fear of FTP processes were important barriers present across all stages. The fragmented structure of local services, along with individual factors such as stigma and confidentiality concerns, further limited the progression of medical students through the candidacy stages.

Relationship to other research

Previous studies and policy frameworks have identified similar barriers to seeking and accessing mental health care for medical students [ 6 , 7 , 8 , 26 , 27 ], focusing primarily on individual barriers such as stigma or FTP concerns. Importantly, our findings reinforce that medical students are reluctant to disclose a mental health problem due to the feared consequences of regulatory FTP proceedings that would lead to dismissal and expulsion. The Candidacy Framework allowed us to go further by understanding how individual and service-level barriers arise and intersect with professional challenges to service provision. Applying the Framework to guide the qualitative analysis also uncovered new and unique challenges across the ‘service-user’ journey. For example, medical students with acute and complex mental health problems may fall through the gaps between primary and secondary healthcare. While there are similar studies in this field, previous findings are based on small focus groups of medical students which do not consider the perspectives of professionals working across healthcare and educational settings. To our knowledge, our study is the first to provide mixed methods findings that represent a diversity of voices and provide deeper insights into the fragmented structure of services, with care providers working across different healthcare organisations and HEIs, which are driven by different priorities. Taken together, these barriers significantly impact on candidacy and mean that medical students may feel unable to seek or access support that is clinically effective, timely and appropriate for their needs.

Limitations and strengths

A strength of this work was that the study protocol and research materials were co-produced by a stakeholder panel of professionals and medical students with lived experience. Data was discussed by the panel to ensure views were robust, accurate and representative of values and needs. This study therefore provides an example of how working in partnership with people with lived experience and professional stakeholders can meaningfully inform our understanding of mental health service delivery and development. Another strength was the triangulation of multiple data sources to understand barriers to service access and delivery. The initial survey data uncovered how mental health symptoms may relate to help-seeking behaviours and service use. After this data was analysed, we determined how these barriers aligned with the Candidacy Framework and professionals’ experiences of service provision.

Surveying and interviewing medical students at one time point does not however, allow for an exploration of the complete student journey across a medicine degree. Potential limitations are the cross-sectional survey design, where a longitudinal approach may have allowed for a more robust view of how help-seeking may change during the academic year. We also acknowledge that the online survey was administered at the tail-end of the COVID-19 Pandemic, which may have accounted for increased psychological symptoms, such as anxiety [ 31 ]. Another limitation is the sole focus on a single UK medical school. Our low response rate may indicate a potential response bias, with medical students who have previously experienced mental health issues being more likely to participate in the survey than those who have not. We aimed for maximal variation by interviewing professionals from a range of settings and selecting medical students with different mental health profiles who had accessed a range of services. However, our findings are limited to a small sample size and reflect local context and policies – particularly in terms of how healthcare systems are configured and their operating conditions.

Implications for healthcare services, policy-makers and further research

Asserting candidacy takes work from the service user, healthcare and University professionals and other stakeholders [ 32 ]. Our findings can help to identify groups of medical students who are at risk of ‘falling through the cracks’ in the system, which is an essential condition to prioritising resource allocation and providing accessible care. In line with guidance from MQ Mental Health Research [ 33 ], policy-makers should aim to improve the accessibility of mental health services by providing integrated high-quality care and prioritising strategies to reduce stigma. For medical schools in particular, stigma reduction strategies should provide clear FTP guidance that supports informed decision-making, personalised planning and seeking timely and appropriate support for mental health symptoms. Universities and healthcare services should further aim to address the gap between primary and secondary services by providing care that is more integrated and coordinated – particularly for medical students with complex and acute mental health problems who, based on our findings, are possibly more at risk of falling between this gap in service provision. The Sheffield Primary and Community Mental Health Transformation Programme [ 34 ] provides a local model of care aiming to inform a new way of delivering adult mental health services and break down barriers between primary and secondary care. More generally, we recommend partnership working between HEIs, healthcare services and medical students to inform service development and delivery.

Future studies should explore the experiences of specific case groups of medical students, particularly those with different types and acuity of mental health symptoms to determine how these factors influence candidacy. The MIND collaboration ( https://doi.org/10.17605/OSF.IO/48WE2 ) is co-producing a process map of existing service pathways to identify gaps along the student journey and is co-designing a toolkit to address some of the touchpoints and barriers identified in this research.

Conclusions

Our findings indicate that fear of FTP processes, along with the fragmented structure of local services and individual factors such as perceived stigma, limit the progression of medical students through the Candidacy Framework. By understanding these barriers and gaps in service provision, Universities and healthcare services can be developed to better to meet medical students’ mental health needs based on their presenting problem and stage of candidacy.

Availability of data and materials

The statistical analysis plan and outputs are included as additional files. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Counselling Centre Assessment of Psychological Symptoms

Fitness to practice

Higher Education Institution

National Health Service

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Hodgson PD, Tazzyman DA, Fryer DK. Evaluation of the Sheffield Primary and Community Mental Health Transformation Programme. 2022.

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Acknowledgements

The authors would like to thank the stakeholder panel of professionals and people with lived experience for their contributions: Dr Helen Crimlisk, Dr Fran Oldale, Dr Emma Broglia, Dr Dominic Strezynski, Adiy Ibrahim, Amran O Jimale, Changmin Doh, Eleanor Morris, Manjeevan Singh, Mohamed Morgan, Oscar Han, Sanjana Mehrotra and Srinath Ravi.

This work was funded by the British Medical Association (BMA) Foundation under The Scholarship Grant (2022). For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.

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Jasmine Young

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ES, JY, MSB, CB and DH conceived and designed the study. ES, NE, JY, CB and DH designed interview guides. ES and NE collected data and conducted the analysis and interpretation. ES, CB and DH conceptualised the theoretical framework. NE prepared the figures. LD conducted the statistical analyses. ES wrote the article and all authors critically revised the paper. All authors read and approved the final manuscript.

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Sheldon, E., Ezaydi, N., Desoysa, L. et al. Barriers to help-seeking, accessing and providing mental health support for medical students: a mixed methods study using the candidacy framework. BMC Health Serv Res 24 , 738 (2024). https://doi.org/10.1186/s12913-024-11204-8

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Mental Health

Poor Mental Health Impacts Adolescent Well-being

Key Takeaways

• The number of adolescents reporting poor mental health is increasing.
• Building strong bonds and connecting to youth can protect their mental health.
• School staff and families can create protective relationships with students and help them grow into healthy adulthood.

Promoting Mental Health and Well-Being in Schools: An Action Guide for School Administrators and Leaders

Learn about school-based strategies and approaches to support student mental health.

Adolescent Mental Health Continues to Worsen

CDC’s Youth Risk Behavior Surveillance Data Summary & Trends Report: 2011-2021 [PDF – 10 MB]  highlights concerning trends about the mental health of U.S. high school students.

• In 2021, more than 4 in 10 (42%) students felt persistently sad or hopeless and nearly one-third (29%) experienced poor mental health.
• In 2021, more than 1 in 5 (22%) students seriously considered attempting suicide and 1 in 10 (10%) attempted suicide.

These data bring into focus the level of distress many students are experiencing.

Some groups are more affected than others.

These feelings of distress were found to be more common among LGBQ+ students, female students, and students across racial and ethnic groups.

• Nearly half (45%) of LGBQ+ students in 2021 seriously considered attempting suicide—far more than heterosexual students.
• Black students were more likely to attempt suicide than students of other races and ethnicities.
• Youth Mental Health: The Numbers

Why Is This a Big Deal?

Poor mental health in adolescence is more than feeling blue. It can impact many areas of a teen’s life.

Youth with poor mental health may struggle with school and grades , decision making, and their health.

Mental health problems in youth often go hand-in-hand with other health and behavioral risks like increased risk of drug use , experiencing violence , and higher risk sexual behaviors  that can lead to HIV, STDs, and unintended pregnancy.

Because many health behaviors and habits are established in adolescence that will carry over into adult years, it is very important to help youth develop good mental health.

Need Support Now?

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The Good News

The good news is that teens are resilient, and we know what works to support their mental health:  feeling  connected  to school and family .

• Fortunately, the same prevention strategies that promote mental health—like helping students feel connected to school/family—help prevent a range of negative experiences, like drug use and violence.
• Building strong bonds and relationships with adults and friends at school, at home and in the community provides youth with a sense of connectedness.
• This feeling of connectedness is important and can protect adolescents from poor mental health, and other risks like drug use and violence.
• Youth need to know someone cares about them. Connections can be made virtually or in person.

There is a Role for Everyone in Supporting Teen Mental Health

As we’ve learned nationally during the COVID-19 pandemic , schools are critical in our communities to supporting children and families. While the expectation is that schools provide education, they also provide opportunities for youth to engage in physical activity and academic, social, mental health, and physical health services, all of which can relieve stress and help protect against negative outcomes.

However, the pandemic disrupted many school-based services, increasing the burden on parents, increasing stress on families, and potentially affecting long-term health outcomes for parents and children alike, especially among families already at risk for negative health outcomes from social and environmental factors.

Support is needed to mitigate these negative outcomes and lessen educational and health disparities.

Critical supports and services need to be comprehensive and community wide and should include:

What schools can do:.

• Implement strategies and approaches that can help prevent mental health problems and promote positive behavioral and mental health of students.
• Help students cope with emergencies and their aftermath.
• Linking students to mental health services.
• Integrating social emotional learning.
• Training staff.
• Supporting staff mental health.
• Reviewing discipline policies to ensure equity.
• Building safe and supportive environments.

What parents and families can do:

• Communicate openly and honestly, including about their values.
• Supervise their adolescent to facilitate healthy decision-making.
• Spend time with their adolescent enjoying shared activities.
• Become engaged in school activities and help with homework.
• Volunteer at their adolescent’s school.
• Communicate regularly with teachers and administrators.

What healthcare providers can do:

• Ask adolescents about family relationships and school experiences as a part of routine health screenings.
• Encourage positive parenting practices .
• Engage parents in discussions about how to connect with their adolescents, communicate effectively, and monitor activities and health behaviors.
• Educate parents and youth about adolescent development and health risks.

More Information

Parents and families may find the following resources helpful to support the mental and emotional well-being of their adolescents:

• CDC Children’s Mental Health
• CDC Mental Health
• School Connectedness
• Social Connection
• Teen Mental Health
• Resources for Coping After Emergencies
• School-Based Physical Activity Improves the Social and Emotional Climate for Learning
• School Nutrition and the Social and Emotional Climate and Learning

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Recent Trends in Mental Health and Substance Use Concerns Among Adolescents

Nirmita Panchal Published: Feb 06, 2024

In light of growing mental health concerns among adolescents in the United States, a National State of Emergency in Child and Adolescent Mental Health was issued in 2021, followed by advisories from the U.S. Surgeon General in 2021 and 2023 . This comes at a time when many adolescents have reported adverse experiences, youth drug overdose deaths have spiked, and gun violence has increased. In 2021, 42% of adolescents reported feelings of sadness and hopelessness – which can be indicative of depressive disorder – up from 28% in 2011. Further, a recent KFF poll found that 55% of the public see youth mental health issues as a crisis in the U.S.; and that many children and teenagers are not able to get the mental health services they need.

Data on youth mental health is limited and when it is available, parents or guardians often complete survey questionnaires on behalf of youth in their household. However, the recently released Teen National Health Interview Survey ( NHIS-Teen ) surveyed adolescents (ages 12-17) directly, which allows for a more direct representation of adolescent mental health. This brief uses the NHIS-Teen data – which was collected for an 18 month period from 2021 to 2022 – to provide an up-to-date analysis of adolescent mental health, utilization of mental health care, and unmet needs and how they vary across demographics, including sex and sexual identity. 1 Other survey data collected directly from adolescent populations, including the Youth Risk Behavior Surveillance System ( YRBSS ) and the National Survey on Drug Use and Health ( NSDUH ), are included to supplement and provide more context.

Key takeaways include:

• In 2021 and 2022, 21% of adolescents reported experiencing symptoms of anxiety in the past two weeks and 17% reported experiencing symptoms of depression. Female and LGBT+ adolescents were more likely than their counterparts to report experiencing anxiety or depression.
• Deaths due to drug overdose among adolescents more than doubled from 2018 (253 deaths) to 2022 (723 deaths). The largest increases in these deaths were among Hispanic and Black adolescents.
• Suicides are the second leading cause of death among adolescents. These deaths peaked in 2018 but have declined in recent years. In 2022, suicide death rates were highest among American Indian and Alaska Native adolescents (22.2 per 100,000) followed by White adolescents (7.2 per 100,000). Adolescent males had higher rates of suicide compared to their female peers (8.1 vs. 3.8 per 100,000) in 2022; however, thoughts of suicide and suicide attempts were higher (and increased faster) for females.
• In 2021 and 2022, 20% of adolescents reported receiving mental health therapy and 14% reported taking prescription medication. In general, LGBT+ and female adolescents were more likely to report receiving treatment than their counterparts.
• Many adolescents reported adverse experiences, including bullying (34%), emotional abuse by a parent (17%), and neighborhood violence (15%) in 2021 and 2022. Ninety-two percent of adolescents reported extended use of screens, which can also negatively impact mental health and well-being.

What share of adolescents experience poor mental health and how does that vary?

Approximately one in five adolescents reported experiencing symptoms of anxiety or depression (Figure 1). In 2021 and 2022, 21% of adolescents reported experiencing symptoms of anxiety in the past two weeks and 17% reported experiencing symptoms of depression. Anxiety and depression can co-occur with other mental health disorders and are associated with  suicide  and  substance use . Additionally, these conditions can impact school attendance and performance among youth.

Female adolescents were more likely than their male peers to report anxiety (31% vs. 12%) and depression (25% vs. 10%) in 2021 and 2022 (Figure 1). These differences among adolescents by sex are consistent with other historical survey data on experiences of poor mental health. In recent years, other indicators of poor mental health, including self-harm and eating disorders, which commonly co-occur with anxiety , have increased, particularly among adolescent females. Analyses of emergency department visits , hospital admissions , and privately-insured youth found that, compared to prior to the pandemic, the presentation of eating disorders increased sharply for adolescent females. Historically, eating disorders affect females more than males. Eating disorders can be very harmful for physical health and even result in death .

In 2021 and 2022, LGBT+ adolescents were more likely than their non-LGBT+ peers to report anxiety (43% vs. 14%) and depression (37% vs. 11%) (Figure 1). Prior survey data has found similar differences in experiences of poor mental health between LGBT+ and non-LGBT+ adolescents.

While data on racial and ethnic groups from NHIS-Teen is not included in this analysis, data from NSDUH and YRBSS shows little variation in mental health conditions among adolescents by racial and ethnic groups. For example, in 2022, 20% of adolescents experienced a major depressive episode in the past year, with no significant differences across racial and ethnic groups. The 2021 YRBSS survey found that the share of Hispanic high school students that reported persistent feelings of sadness and hopelessness (46%) – which can be indicative of depressive disorder – was slightly higher than the share reported by their White (41%), Black (39%), and Asian peers (35%). However, mental health conditions among adolescents of color may be underreported as a result of underdiagnosis , gaps in culturally sensitive  mental health care ,  structural barriers , and stigma associated with accessing care. Note that the NHIS-Teen survey data does not disaggregate data on non-Hispanic adolescents by racial groups and, therefore, was not included in this analysis.

How have substance use and related deaths among adolescents changed in recent years?

Deaths due to drug overdose among adolescents more than doubled since the onset of the COVID-19 pandemic, largely driven by the synthetic opioid , fentanyl . After remaining stable for several years, KFF analysis of CDC WONDER data found that drug overdose deaths among adolescents increased from 253 deaths in 2018 to 723 deaths in 2022 (Figure 2). During the same period, the share of these overdose deaths involving opioids increased from 57% to 78%.

Although White adolescents continue to account for the largest share of adolescent drug overdose deaths, Black and Hispanic adolescents have experienced the fastest increase in these deaths in recent years. In 2022, White adolescents accounted for 49% of total adolescent drug overdose deaths, down from 63% in 2018. 2 This decrease reflects the rapid increase in drug overdose deaths among adolescents of color since the onset of the pandemic. By 2022, the drug overdose death rate of both Hispanic and Black adolescents (3.3 and 2.8 per 100,000) surpassed the overdose death rate of White adolescents (2.7 per 100,000) (Figure 3). Further, these drug overdose death rates increased more than fourfold among Hispanic and Black adolescents compared to prior to the pandemic.

Since the COVID-19 pandemic began, drug overdose deaths increased for both adolescent males and females, with an initial spike among males. From 2018 to 2022, the drug overdose death rate more than doubled among adolescent males (from 1.1 to 3.0 per 100,000) and females (from 1.0 to 2.5 per 100,000) (Figure 3).

Although drug overdose deaths among adolescents have increased, their use of some substances has declined over time. YRBSS data from 2011 to 2021 shows declines in adolescent use of several substances, including current alcohol use (from 39% to 23%), current marijuana use (from 23% to 16%), and ever used illicit drugs (from 19% to 13%). However, findings on whether substance use has increased among adolescents during the pandemic are mixed. Some research has shown that substance use decreased in 2021 among adolescents and then largely held steady in 2022. Other research found that among high school students who used substances prior to the pandemic, nearly one in three reported increases in substance use in 2021. Early initiation of substance use is associated with increased risk of addiction later in life.

Mental health and substance use issues can often co-occur among adolescents. Data from NSDUH found that adolescents experiencing a past year major depressive episode in 2022 were more likely than peers to have used illicit drugs (26% vs 12%) and marijuana (22% vs 9%) in the past year, misused opioids (3% vs 1%) in the past year, and engaged in binge drinking (6% vs 3%) in the past month. In total, 4% of adolescents reported both a past year major depressive episode and substance use disorder in 2022. A recent analysis found that 41% of youth ages 10-19 that died from a drug overdose between 2019 and 2021 had a documented mental health condition.

How have suicide and self-harm among adolescents changed in recent years?

In the past decade, CDC data show that adolescent deaths due to suicide increased and peaked in 2018 (1,750 deaths) before slowing and declining by 2022 (1,540 deaths). Suicide remains the second leading cause of death among adolescents. 3 However, from 2021 to 2022, the adolescent suicide death rate decreased by 8% (from 6.5 to 6.0 per 100,000) while the total population suicide death rate slightly increased . It is possible that some suicides are misclassified as drug overdose deaths since it can be difficult to determine whether drug overdoses are intentional . Forty-four percent of adolescent suicides were by firearm in 2022, compared to 40% in 2012. 4

The rate of suicide deaths is increasing faster among adolescents of color compared to their White peers. Suicide death rates remain highest among American Indian and Alaska Native (AIAN) adolescents; in 2022, the death rate for AIAN youth was three times higher than White youth (22.2 vs. 7.2 per 100,000, respectively; Figure 4). Although their suicide death rates were lower than White adolescents, Black, Asian, and Hispanic adolescents experienced larger increases in these death rates from 2012 to 2022 (129%, 48%, 30%, respectively; Figure 4) compared to their White peers (26%). Further, in 2021, Black high school students were more likely to report attempting suicide than their Asian, Hispanic, and White peers.

Among adolescents, male suicide rates are more than double the rates among females. Although the suicide death rate among adolescent females has increased faster than their male counterparts over the past decade, the adolescent female suicide death rate remains significantly lower than the death rate of their male peers (3.8 vs. 8.1 per 100,000 in 2022) (Figure 4). However, the share of adolescent females reporting serious thoughts of suicide remains higher and has increased faster over time (from 19% in 2011 to 30% in 2021) compared to adolescent males (from 13% in 2011 to 14% in 2021). Similar trends were seen in suicide attempts: from 10% in 2011 to 13% in 2021 among adolescent females, and from 6% to 7% over the same period for adolescent males. Additionally, as the pandemic progressed, emergency department visits for suicide attempts increased among adolescents, primarily driven by females.

LGBQ+ adolescents are more likely to experience suicidal thoughts compared to their heterosexual peers. Data from YRBSS found that in 2021 , higher shares of LGBQ+ adolescents reported serious thoughts of suicide (45% vs. 15%) and suicide attempts (22% vs. 6%) compared to heterosexual adolescents. 5 Data on suicide deaths by LGBQ+ identity were not available.

What share of adolescents report receiving mental health treatment in the past year and how does that vary?

Access to and sources of mental health services.

Among all adolescents, 20% reported receiving mental health therapy or counseling and 14% reported taking prescription medication for mental health in the past year (Figure 5). LGBT+ adolescents were more likely to report receiving mental health therapy or counseling (35%) and prescription medication (24%) for mental health in the past year than their counterparts (15% and 11%, respectively). Higher shares of female adolescents reported receiving mental health therapy or counseling compared to their male peers (24% vs. 16%).

Among adolescents with a past year major depressive episode, mental health services were most often accessed through outpatient care and telehealth. Data from NSDUH found that in 2022, 19.5% of adolescents (or 4.8 million) had a past year major depressive episode. Major depressive episode refers to a period of at least two weeks when an individual experienced a depressed mood or loss of interest or pleasure in daily activities and had a majority of specified depression symptoms. Among these adolescents with a past year major depressive episode, 48% received mental health services in an outpatient setting, which includes general medical and education settings (Figure 6). Thirty-four percent of adolescents with a past year major depressive episode received mental health care via telehealth (care received via phone or video from a therapist or other health care professional) in 2022. Additionally, 8% of adolescents with a past year major depressive episode accessed mental health care at emergency departments. There has been an uptick in mental health-related emergency visits in recent years; however, emergency departments may have limited capacity to address psychiatric illnesses.

Unmet need for mental health services

Although some adolescents received mental health care, 20% reported not receiving the mental health therapy they needed because of cost, fear of what others would think, and/or they did not know how to get help (Figure 7). This lack of needed therapy or counseling was more pronounced among female (32%) and LGBT+ adolescents (38%). While data on racial and ethnic groups from NHIS-Teen is not included in this analysis, other KFF analyses have found that receipt of mental health treatment is generally lower among people of color compared to their White peers.

Other factors that may contribute to limited mental health care access among adolescents include insurance barriers , a lack of providers , and the absence of  culturally competent care . Additionally, in light of the COVID-19 pandemic, access and utilization of mental health care may have worsened . Among Medicaid and CHIP beneficiaries, utilization of mental health services  declined  by 25% for beneficiaries 18 and younger from March 2020 to July 2022 compared to prior to the pandemic; and utilization of substance use disorder services  declined by 31% for beneficiaries ages 15-18 during the same period. Nearly  two out of five  children under the age of 18 in the U.S. are Medicaid or CHIP beneficiaries.

Although adolescent drug overdose deaths have increased, access to buprenorphine and residential addiction treatment facilities is limited. The dispensing of buprenorphine , a medication approved to treat opioid use disorder, is low among adolescents . Additionally, many residential addiction treatment facilities do not have availability for adolescents and are costly. These facilities often do not provide buprenorphine to adolescents with opioid use disorder.

If untreated, mental health conditions can persist into adulthood and limit quality of life. In 2021 and 2022, just over half of teens ( 55% ) reported discussing their mental or emotional health with their health care provider in the past year; and only 20% reported discussing transitions in their health care services that will go into effect when they turn 18.

What experiences among adolescents may negatively impact their mental health and well-being?

Many adolescents report negative experiences that can impact their mental health and well-being, including bullying (34%) and, specifically, electronic bullying (11%) (Figure 8). Higher shares of LGBT+ adolescents reported experiencing bullying (49%), and electronic bullying (23%), compared to their peers (28% and 8%, respectively). Bullying can increase the risk of mental health conditions, substance use, and self-harm. Electronic bullying may be associated with depression among youth and is more often experienced by female and sexual minority youth compared to their peers.

Adolescents are also spending more  time  on  screens , including social media, which may lead to depression and poor well-being. Ninety-two percent of adolescents reported at least two hours of weekday screentime not associated with schoolwork. Emerging research has found that both smartphone use and social media use may be associated with poor well-being among youth, with a higher risk of depression for female adolescents. Social media use can also lead to difficulties with sleep and maintaining attention.

Many adolescents report adverse experiences which can lead to both mental and physical health concerns. In 2021 and 2022, 21% and 18% of adolescents reported living with a household member experiencing mental illness or substance use issues, respectively; 17% reported emotional abuse by a parent or adult in their household; 15% reported neighborhood violence; and 11% reported having a parent in jail or prison. Adverse childhood experiences are linked to mental illness, substance use, and chronic physical health problems in adolescence and can extend into adulthood. Social supports , including relationships with peers, can be a protective factor among adolescents in the face of adverse experiences. However, only 50% of adolescents  reported  having peer support “a lot of the time” in 2021 and 2022.

Gun violence continues to rise and may lead to negative mental health impacts among children and adolescents.  An increasing number of children and adolescents have been exposed to gun violence in recent years. School shootings  have increased and, beginning in 2020,  firearms  became the leading cause of death among children and teens ages 19 and below. Children and adolescents may experience negative mental health impacts, including symptoms of anxiety, in response to school shootings and  gun-related injuries or deaths  in their  communities . Youth antidepressant use has also been shown to increase following exposures to fatal school shootings.

Looking Ahead

National efforts to address youth mental health concerns include recommendations for mental health screenings and strengthening social media safety protocols, and federal legislation to expand school-based mental health services. The U.S. Preventive Services Task Force put forth recommendations for youth anxiety and depression screenings and the U.S. Surgeon General also issued several advisories, including an advisory on youth mental health and social media that highlights potential solutions for strengthening social media safety protocols and encouraging digital and media literacy. Recently, the U.S. Senate Judiciary Subcommittee held a hearing on the impact of social media on youth mental health and well-being. In light of the increase in mental health-related pediatric ED visits, the American Academy of Pediatrics, the American College of Emergency Physicians and the Emergency Nurses Association released a statement including recommendations to improve care for mental health emergencies. Recent legislation allows for the expansion of school-based mental health care through a number of strategies, including growing the number of school-based mental health providers, leveraging Medicaid to further build out services, and providing trauma care to students.

At the state and local level, initiatives to improve access to youth mental health care include promoting school-based Medicaid behavioral health services and connecting youth to virtual care at no cost. State Medicaid programs have taken a variety of approaches to promote access to Medicaid behavioral health services provided in schools. These include working closely with local education agencies, taking advantage of the  reversal  of the free care policy, and increasing reimbursements for school-based providers. Local initiatives have also been proposed, including a partnership with New York City’s Department of Health and Mental Hygiene and the mental health app Talkspace to allow for teenagers to connect virtually with licensed therapists at no cost. However, the quality and clinical effectiveness of emerging mental health apps remains unclear . Looking ahead, data on adolescent populations will be pivotal in understanding how to further address and mitigate rising mental health and substance use concerns.

This work was supported in part by the Well Being Trust. KFF maintains full editorial control over all of its policy analysis, polling, and journalism activities.

The NHIS-Teen data does not disaggregate data on non-Hispanic adolescents by racial groups and, therefore, was not included in this analysis.

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Drug overdose death distributions by race and ethnicity do not include non-Hispanic individuals of more than one race. KFF analysis of CDC WONDER. Accessed at: https://wonder.cdc.gov/mcd-icd10-provisional.html

KFF analysis of Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. Web-based Injury Statistics Query and Reporting System (WISQARS). Accessed at: https://webappa.cdc.gov/sasweb/ncipc/leadcause.html

KFF analysis of Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. Web-based Injury Statistics Query and Reporting System (WISQARS). Accessed at: https://wisqars.cdc.gov/fatal-reports

The 2021 YRBSS did not include questions on gender identity.

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UK medical students' mental health during the COVID-19 pandemic: a qualitative interview study

Affiliations.

• 1 Research Department of Medical Education, UCL Medical School, University College London, London, UK [email protected].
• 2 Research Department of Medical Education, UCL Medical School, University College London, London, UK.
• 3 Centre for Healthcare Innovation Research, Department of Health Services Research and Management, City University, London, UK.
• PMID: 37076141
• PMCID: PMC10124246
• DOI: 10.1136/bmjopen-2022-070528

Objectives: To understand the impact of COVID-19 on medical students with mental health problems.

Design: Qualitative study employing in-depth semistructured interviews with medical students which were analysed using reflexive thematic analysis.

Setting and participants: A purposive sample of 20 students originating from 8 geographically spread UK medical schools were selected, representing various mental health issues and demographic characteristics.

Results: Three themes were identified: (1) medical schools' response to the pandemic-schools increased awareness-raising of mental health support and increased flexibility in regards to academic requirements; (2) disruption to the medical degree-COVID-19 brought change and uncertainty to medical education and missed learning opportunities reduced students' confidence and (3) psychological consequences of the pandemic-COVID-19 had a negative impact on mental health, most notably raising stress and anxiety but also triggering new or existing conditions.

Conclusions: While there were many negative aspects of the pandemic for medical students experiencing mental ill health, there were also positives. Students felt that the increased focus on mental health support during the pandemic had reduced stigma towards mental health. Given stigma has been identified as a key barrier for help-seeking in medical students, future research should investigate the longer-term impacts of the pandemic and whether medical students are more likely to seek help for mental health difficulties postpandemic.

Keywords: Covid-19; medical education & training; mental health; qualitative research.

© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

PubMed Disclaimer

Conflict of interest statement

Competing interests: None declared.

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• The impact of COVID-19 on medical students. Wang W, Li G, Lei J. Wang W, et al. GMS J Med Educ. 2024 Feb 15;41(1):Doc10. doi: 10.3205/zma001665. eCollection 2024. GMS J Med Educ. 2024. PMID: 38504857 Free PMC article. Review.
• Positive Mental Health of Migrants in the UK during COVID-19: A Review. Kotera Y, Adam H, Kirkman A, Aledeh M, Brooks-Ucheaga M, Todowede O, Rennick-Egglestone S, Jackson JE. Kotera Y, et al. Int J Environ Res Public Health. 2023 Nov 10;20(22):7046. doi: 10.3390/ijerph20227046. Int J Environ Res Public Health. 2023. PMID: 37998277 Free PMC article. Review.
• Qualitative study to explore UK medical students' and junior doctors' experiences of occupational stress and mental health during the COVID-19 pandemic. Tawse J, Demou E. Tawse J, et al. BMJ Open. 2022 Dec 12;12(12):e065639. doi: 10.1136/bmjopen-2022-065639. BMJ Open. 2022. PMID: 36523252 Free PMC article.
• Exploring the psychological impact of working during COVID-19 on medical and nursing students: a qualitative study. Griffin L, Riley R. Griffin L, et al. BMJ Open. 2022 Jun 23;12(6):e055804. doi: 10.1136/bmjopen-2021-055804. BMJ Open. 2022. PMID: 35738645 Free PMC article.
• Factors affecting the mental health of pregnant women using UK maternity services during the COVID-19 pandemic: a qualitative interview study. McKinlay AR, Fancourt D, Burton A. McKinlay AR, et al. BMC Pregnancy Childbirth. 2022 Apr 12;22(1):313. doi: 10.1186/s12884-022-04602-5. BMC Pregnancy Childbirth. 2022. PMID: 35413807 Free PMC article.
• Medical student wellbeing during COVID-19: a qualitative study of challenges, coping strategies, and sources of support. West HM, Flain L, Davies RM, Shelley B, Edginton OT. West HM, et al. BMC Psychol. 2024 Mar 28;12(1):179. doi: 10.1186/s40359-024-01618-8. BMC Psychol. 2024. PMID: 38549145 Free PMC article.
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Addressing the unprecedented behavioral-health challenges facing Generation Z

Nearly two years after the COVID-19 pandemic began in the United States, Gen Zers, ranging from middle school students to early professionals, are reporting higher rates of anxiety, depression, and distress than any other age group. 1 Ages for Generation Z can vary, with some analysis including ages as young as nine. In this article, we focus on those between the ages of 16 and 24, and define millennials as 25 to 40; Ramin Mojtabai and Mark Olfson, “National trends in mental health care for US adolescents,” JAMA Psychiatry , March 25, 2020, Volume 77, Number 7; Martin Seligman, The Optimistic Child: A Revolutionary Approach to Raising Resilient Children , Boston, MA: Mariner Books, 2007; Gen Z respondents are 1.5 times as likely to report having felt anxious or depressed, compared with the average respondent, according to the McKinsey Consumer Health Insights Survey, conducted in June 2021—a nationally representative survey of 2,906 responses, including 316 Gen Z responses. The mental-health challenges among this generation are so concerning that US surgeon general Vivek Murthy issued a public health advisory on December 7, 2021, to address the “youth mental health crisis” exacerbated by the COVID-19 pandemic. 2 Protecting youth mental health: US surgeon general’s advisory , Office of the Surgeon General, December 7, 2021.

About the authors

The article is a collaborative effort by Erica Coe , Jenny Cordina , Kana Enomoto , Raelyn Jacobson , Sharon Mei, and Nikhil Seshan, representing views of the McKinsey’s Healthcare Systems & Services and Public & Social Sector Practices.

A series of consumer surveys and interviews conducted by McKinsey indicate stark differences among generations, with Gen Z  reporting the least positive life outlook, including lower levels of emotional and social well-being than older generations. One in four Gen Z respondents reported feeling more emotionally distressed (25 percent), almost double the levels reported by millennial and Gen X respondents (13 percent each), and more than triple the levels reported by baby boomer respondents (8 percent). 3 These research efforts have been focused on Gen Zers between the ages of 16 and 24 when compared with samples of millennials (aged 25 to 40), Gen Xers (aged 41 to 56), and baby boomers (aged 57 to 76). And the COVID-19 pandemic has only amplified this challenge (see sidebar, “The disproportionate impact of the COVID-19 pandemic”). While consumer surveys are, of course, subjective and Gen Z is not the only generation to experience distress, employers, educators, and public health leaders may want to consider the sentiment of this emerging generation as they plan for the future.

The disproportionate impact of the COVID-19 pandemic

While Gen Z is less vulnerable to the physical impacts of the COVID-19 pandemic, they bear unique burdens due to their life stage, including emotional stress and grief from the pandemic, high rates of job loss and unemployment, and educational challenges from remote or interrupted learning. The effects of the pandemic may be especially felt by recent college graduates, many of whom have encountered difficulties finding jobs, had their previously secured job offers rescinded, or were unable to apply to graduate school due to the timing of the lockdowns in March 2020. In April 2020, workers aged 18 to 24 faced 27 percent unemployment, with 13 percent of this segment ceasing to look for work. While employment has largely recovered, this segment has exited the workforce at twice the rate of other age groups  since the start of the pandemic. The inequitable impact of the pandemic by race extends to Gen Z employment as well, where Black, Hispanic/Latino, and Asian American and Pacific Islander (AAPI) workers aged 18 to 24 faced up to 1.8 times the unemployment rates of their White counterparts. 1 McKinsey analysis of the US Census Bureau Current Population Survey as of November 2020.

In our sample, Gen Z respondents were more likely to report having been diagnosed with a behavioral-health condition (for example, mental or substance use disorder) than either Gen Xers or baby boomers. 4 Gen Z respondents were 1.4 to 2.3 times more likely to report that they had been diagnosed with a mental-health condition and 1.9 to 4.1 times more likely to be diagnosed with a substance-use disorder than both Gen Xers and baby boomers. Based on the McKinsey Consumer Behavioral Health Survey conducted in November–December 2020—a nationally representative survey of 1,523 responses, including an oversample of Gen Z respondents (aged 16 to 24, n = 874). Gen Z respondents were also two to three times more likely than other generations to report thinking about, planning, or attempting suicide in the 12-month period spanning late 2019 to late 2020.

Gen Z also reported more unmet social needs than any other generation. 5 Also referred to as social determinants of health or social needs, including income, employment, education, food, housing, transportation, social support, and safety. These basic needs, if unmet, can negatively affect health. In addition, factors such as race, ethnicity, gender and sexual orientation, disability, and age can influence health status. Fifty-eight percent of Gen Z reported two or more unmet social needs, compared with 16 percent of people from older generations. These perceived unmet social needs, including income, employment, education, food, housing, transportation, social support, and safety, are associated with higher self-reported rates of behavioral-health conditions. As indicated in a recent nationwide survey, people with poor mental health were two times as likely to report an unmet basic need as those with good mental health, and four times as likely to have three or more unmet basic needs. 6 2019 McKinsey Social Determinants of Health Survey, n = 2,010, where respondents included those with Medicare or Medicaid coverage, individuals with coverage through the individual market who had household incomes below 250 percent of the federal poverty level, and individuals who were uninsured and had household income below 250 percent of the federal poverty level.

As these young adults work to develop their resilience, Gen Zers may seek out the holistic approach to health they have come to expect, which includes physical health, behavioral health, and social needs, as future students, employees, and customers.

Characteristics of Gen Z consumers in the healthcare ecosystem

Gen Z’s specific needs suggest that improving their behavioral healthcare will require stakeholders to increase access and deliver appropriate, timely services.

Gen Z is less likely to seek help

Gen Z respondents were more likely to report having a behavioral-health diagnosis but less likely to report seeking treatment compared with other generations (Exhibit 1). For instance, Gen Z is 1.6 to 1.8 times more likely to report not seeking treatment for a behavioral-health condition than millennials. There are several factors that may account for Gen Z’s lack of seeking help: developmental stage, disengagement from their healthcare, perceived affordability, and stigma associated with mental or substance use disorders within their families and communities. 7 Before age 25, the human brain is not fully developed. Awareness of long-term consequences and the ability to curb impulsive behavior are some of the last functions to mature. Thus, adolescents and young adults, across generations and not just Gen Z, may be less likely to engage in activities such as routine or preventive healthcare. For more, see Investing in the health and well-being of young adults , Institute of Medicine and National Research Council, 2015.

Gen Z respondents identified as less engaged in their healthcare than other respondents (Exhibit 2). About two-thirds of Gen Z respondents fell into lower engagement segments of healthcare consumers, compared with one-half of respondents from other generations. Gen Z and other people in these less engaged segments reported that they feel less in control of their health and lifespan, are less health-conscious, and are less proactive about maintaining good health. One-third of Gen Z respondents fell into the least engaged segment, who reported the lowest motivation to improve their health and the least comfort talking about behavioral-health challenges with doctors. 8 Disadvantaged, disconnected users are more resigned to their health and less engaged and active in improving it. They value convenience but are often not engaged digitally.

Another driver for Gen Z’s reduced help-seeking may be the perceived affordability of mental-health services. One out of four Gen Z respondents said they could not afford mental-health services, which had the lowest perceived affordability of all services surveyed. 9 Services surveyed include healthcare, health insurance, internet services, necessary transportation, financial services, housing, and nutritious food. Across the board, Americans with mental and substance use disorders bear a disproportionate share of out-of-pocket healthcare costs for a range of reasons, including the fact that many behavioral-health providers do not accept insurance . “I found the perfect therapist for me but I couldn’t afford her, even with insurance,” said one Gen Z respondent. “The absolute biggest barrier to gaining mental-health treatment has been financial,” added another.

In addition, stigma associated with mental and substance use disorders and a lack of family support may be a substantial barrier in seeking mental healthcare. Many Gen Zers rely on parents for transportation or health insurance and may fear interacting with their parents about mental-health topics. This factor is particularly relevant for communities of color, who report perceiving a higher level of stigma associated with behavioral-health conditions. 10 Mental health: Culture, race, and ethnicity; A supplement to mental health; A report of the surgeon general , US Department of Health and Human Services, August 2001: A 1998 study cited in the supplement found that only 12 percent of Asians would mention their mental-health problems to a friend or relative (compared with 25 percent of Whites), only 4 percent of Asians would seek help from a psychiatrist or specialist (compared with 26 percent of Whites), and only 3 percent of Asians would seek help from a physician (compared with 13 percent of Whites). Children of immigrants also may internalize guilt because of their parents’ sacrifices or may have behavioral-health concerns minimized by their parents, who may state or think their children “have it much easier” than they did growing up. 11 Mental Health America , “To be the child of an immigrant,” blog entry by Kenna Chick, accessed December 1, 2021.

Gen Z relies on emergency care, social media, and digital tools when they do seek help

When they do seek support for behavioral-health issues, Gen Z may not be turning to regular outpatient mental-health services and instead may rely on emergency care, social media, and digital tools .

Gen Zers rely on acute sites of care more often than older generations, with Gen Z respondents one to four times more likely to report using the ER, and two to three times more likely to report using crisis services or behavioral-health urgent care in the past 12 months. Gen Z also makes up nearly three-quarters of Crisis Text Line’s users. 12 Everybody hurts 2020: What 48 million messages say about the state of mental health in America , Crisis Text Line, February 10, 2020. One Gen Z respondent expressed her frustration, saying, “Seems [like the] only option is an emergency room visit, otherwise I have to wait weeks to see a psychiatrist.”

Almost one in four Gen Zers also reported that it is “extremely” or “very” challenging to get help during a behavioral-health crisis. This lack of access is concerning for a generation two to three times more likely to report seeking treatment in the past 12 months for suicidal ideation or attempted suicide, than any other generation.

Many Gen Zers also indicated their first step in managing behavioral-health challenges was going to TikTok or Reddit for advice from other young people, following therapists on Instagram, or downloading relevant apps. This reliance on social media may be due, in part, to the provider shortages in many parts of the country: 64 percent of counties in the United States have a shortage of mental-health providers. Furthermore, 56 percent of counties in the United States are without a psychiatrist (corresponding to 9 percent of the total population), and 73 percent of counties are without a child and adolescent psychiatrist (corresponding to 19 percent of the total population). 13 Oleg Bestsennyy, Greg Gilbert, Alex Harris, and Jennifer Rost, “ Telehealth: A quarter-trillion-dollar post-COVID-19 reality ?,” McKinsey, July 9, 2021; Vulnerable Populations dashboard, McKinsey’s Center for Societal Benefit through Healthcare, accessed December 1, 2021.

Gen Z is less satisfied with the behavioral-health services they receive

Gen Zers say the behavioral healthcare system overall is not meeting their expectations—Gen Zers who received behavioral healthcare were less likely to report being satisfied with the services they received than other generations. For example, compared with older generations, Gen Z reports lower satisfaction with behavioral-health services received through outpatient counseling/therapy (3.7 out of 5.0 for Gen Z, compared with 4.1 for Gen X) or intensive outpatient (3.1 for Gen Z, compared with 3.8 for older generations). 14 Mean differences are significantly different, at a 90 percent confidence level. One Gen Z respondent said, “Struggling to find a psychologist whom I was comfortable with and cared enough to remember my name and what we did the week before” was the most significant barrier to care. Another said, “I have trust issues and find it difficult to talk with therapists about my problems. I also had a very bad experience with a therapist, which made this problem worse.”

Although we have seen high penetration of telehealth in psychiatry (share of telehealth outpatient and office visits claims were at 50 percent in February 2021), 15 Vulnerable Populations: Data Over Time Database, McKinsey Center for Societal Benefit through Healthcare, April 2021. Gen Z has the lowest satisfaction with tele-behavioral health (Gen Z rates their satisfaction with telehealth at a 3.8 out of 5.0, compared with older generations, who rate it 4.1) and digital app/tools (3.5 out of 5.0 for Gen Z, compared with 4.0 for older generations). 16 Mean differences are significantly different, at a 90 percent confidence level. Around telehealth, Gen Zers cited reasons for dissatisfaction such as telehealth therapy feeling “less official” or “less professional,” as well as more difficult to form a trusting connection with a therapist. For apps, Gen Z respondents noted a lack of personalization, as well as a lack of diversity—both in terms of the racial and ethnic diversity of the stories they presented, and in the problems that the apps offered tools to address. In creating and improving behavioral-health tools, it is crucial to employ a user-centered design approach to develop functionality and experiences that Gen Zers actually want.

In creating and improving behavioral-health tools, it is crucial to employ a user-centered design approach to develop functionality and experiences that Gen Zers actually want.

Gen Z cares about diversity when choosing a healthcare provider

Racial and ethnic diversity in the behavioral-health workforce is also important. According to McKinsey’s COVID-19 Consumer Survey, racial and ethnic minority respondents reported valuing racial and ethnic diversity when choosing a physician, citing their physician’s race more frequently than White respondents as a consideration. 17 Thirteen percent of Black respondents, 9 percent of Asian respondents, and 8 percent of Hispanic/Latino respondents cited their physician’s race when selecting the physicians that they see, compared with 4 percent of Whites. Because Gen Z cares deeply about diversity, there are opportunities to integrate care and early intervention by offering a more racially and ethnically diverse behavioral-health workforce and culturally relevant digital tools. 18 According to surveys conducted by the Pew Research Center, most Gen Zers see the country’s growing racial and ethnic diversity as a good thing: Ruth Igielnik and Kim Parker, “On the cusp of adulthood and facing an uncertain future: What we know about Gen Z so far,” Pew Research Center, May 14, 2020.

Potential stakeholder actions to address the needs of Generation Z

In our article “ Unlocking whole person care through behavioral health ,” we outline six potential actions integral to improving the quality of care and experience for millions with behavioral-health conditions. Many of those levers apply to Gen Z, but further tailoring is needed to best meet the needs of this emerging generation. Promising areas to explore could include the emerging role of digital and telehealth; the need for stronger community-based response to behavioral-health crises; better meeting the needs of Gen Z where they live, work, and go to school; promoting mental-health literacy; investing in behavioral health at parity with physical health; and supporting a holistic approach that embraces behavioral, physical, and social aspects of health.

Need for action now

Gen Z is our next generation of leaders, activists, and politicians; many of them have already taken on adult responsibilities as they start climate movements, lead social justice marches, and drive companies to align more closely with their values. Healthcare leaders, educators, and employers all have a role to play in supporting the behavioral health of Gen Z. By taking a tailored, generational approach to designing messages, products, and services, stakeholders can meaningfully improve the behavioral health of Gen Z and help them achieve their full potential. This investment could be viewed as a down payment on our future that will bear social and economic returns for years to come.

Erica Coe is a partner in McKinsey’s Atlanta office and coleads the Center for Societal Benefit through Healthcare, Jenny Cordina is a partner in the Detroit office and leads McKinsey’s Consumer Health Insights research, Kana Enomoto is a senior expert in the Washington, DC, office and coleads the Center for Societal Benefit through Healthcare, Raelyn Jacobson is an associate partner in the Seattle office, Sharon Mei is an expert in the New York office, and Nikhil Seshan is a consultant in the Philadelphia office.

The authors wish to thank Tamara Baer, Eric Bochtler, Emma Dorn, Erin Harding, Brad Herbig, Jimmy Sarakatsannis, and Boya Wang for their contributions to this paper.

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A Simple Method for Assessing the Mental Health Status of Students in Higher Education

Éva bíró.

1 Division of Health Promotion, Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, H-4028 Debrecen, Hungary; [email protected]

Róza Ádány

2 Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, H-4028 Debrecen, Hungary

Karolina Kósa

Mental health problems are common among students in higher education all over the world, so identifying those who are at higher risk would allow the targeted provision of help. Our goal was to develop an assessment tool to identify students at risk for vulnerable mental health status. This tool was created from the 12-item General Health Questionnaire and Antonovsky’s abbreviated sense of coherence scale and was tested to distinguish between those with high or low mental resilience. Predictive ability was characterized by likelihood ratios taking the Beck Depression Inventory and perceived health as references. One-quarter (95% CI 21.1% to 29.7%) of the students had been in vulnerable mental health characterized by low sense of coherence and high distress, whereas 28.4% (95% CI 24.2% to 33.1%) seemed resilient, having high sense of coherence and low distress. The high negative predictive value of the assessment tool reliably identified resilient students in comparison with both the Beck Depression Inventory (98.6%) and perceived health status (83.9%). Use of the assessment tool is recommended for students to distinguish between those at decreased and increased risk in terms of mental health. Mental health services should be offered to students at higher risk.

1. Introduction

Early adulthood is a critical period in terms of developing the habits, self-image and social relations that are characteristic of individuals in adult life. Approximately three-fourths of all lifetime mental disorders start by the mid-20s, and the median age for substance disorders falls between 18 and 29 years of age [ 1 ].

Almost half of college-age youth had a psychiatric disorder according to an USA study, the most prevalent being some type of anxiety disorder. While the adjusted prevalence of substance use between college students and their non-college peers was no different, the former were significantly less likely to receive treatment for alcohol and drug use disorders compared to their peers not in college [ 2 ]. Another, more recent USA study found depression to be the most prevalent condition, with 17% of college students screening positive for depression [ 3 ]. Studies from countries other than the USA also support the notion that college students have a higher burden of mental distress [ 4 ] compared to their peers. This was shown among medical students in 29 studies conducted in Europe and English-speaking countries outside of North America [ 5 ]. A survey of 32% of colleges in the UK found an increase of students with disclosed mental health issues in the past 3 years [ 6 ]. Mental health problems have been prevalent in college students, including substance use, anxiety and mood disorders [ 7 ].

In spite of the increasing numbers of students suffering from mental health problems [ 8 , 9 ], attitudes towards seeking mental health services have become negative among American university students over the past 40 years [ 10 ]. Nearly one-fifth of college students thought that it was better not to disclose mental illness according to a recent study [ 11 ]. Moreover, current mental health practices are misguided [ 12 ] because they tend to focus only on those who already have mental disorders whereas the majority of new cases arise from the general population, so more effective mass population strategies should be followed [ 13 ]. That is, non-stigmatizing services should be offered to those not yet having manifest signs of a mental disorder who are most likely to benefit from them.

Early identification and stratification of youth at various levels of risk enables the development of targeted interventions. Resilient groups (those at decreased risk) could be identified by the salutogenic approach of Antonovsky which focuses on the generalized resistance resources of individuals facilitating their capacity to effectively cope with stressors. Antonovsky’s sense of coherence (SoC) reflects a person’s view of life and capacity to respond to stressful situations, contributing to health [ 14 , 15 ]. Strong positive relation between SoC and perceived good health as well as mental health [ 16 , 17 ] and physical health [ 18 ] have been uncovered. The salutogenic approach could be applied in universities by focusing attention to vulnerable students while also offering help to resilient and effectively coping students in case they need it [ 19 ].

Mental health problems, if left unrecognized and untreated, may result in failed exams or dropping out, and even in attempted or completed suicide as well as engagement in risky behaviors leading to serious injury, disability, or death [ 20 ]. Identification of resilient students as opposed to those who exhibit signs of mental strain at an early stage would enable a more effective use of limited resources by addressing problems as early as possible among those in greatest need to prevent the development of full-blown mental problems.

Based on the results of previous [ 21 , 22 ] and present surveys, and in line with the recommendation of Huppert regarding the use of an universal approach to reduce the number of people with mental disorders [ 12 ], we aimed at developing an assessment tool to identify vulnerable as well as resilient college students in terms of mental health problems.

2. Materials and Methods

2.1. study population.

A cross-sectional study was carried out among students of public health, nursery education, and physiotherapy of the University of Debrecen, Hungary in 2008, 2009 and 2010, respectively. The study population consisted of all full-time students registered at the courses of public health ( N = 194), nursery education ( N = 168) and physiotherapy at years 1–3 ( N = 153).

Data of the general population were taken from a mental health survey representative of the adult Hungarian population that was carried out in a sample of 1200 persons by the Median Polling Institute in 2010 following a multistage stratified cluster sampling.

2.2. Data Collection

The timing of data collection on mental health is critical among university students because their stress level fluctuates during the academic year. A potential source of bias might be excessive stress close to or during the exam period, so data were collected in mid-semester in order to reduce this type of bias. Each student was invited in person after class to fill a paper-based, self-administered, anonymous questionnaire. The distribution and recollection of questionnaires was carried out by student volunteers from courses not involved in the survey in order to avoid any pressure for participation.

The research was carried out in accordance with the Helsinki Declaration. Ethical permission was issued by the Regional and Institutional Commission on Research Ethics of the Medical and Health Science Centre of the University of Debrecen, Hungary (DEOEC RKEB/IKEB: 2506-2006). The students were informed in writing and in person that participation was anonymous and voluntary, and they had the right to refuse to participate. The participants had given verbal consent for their data to be used in the research. No personal data were collected so a consent form was not requested to be signed by the ethics committee.

Scales of the Questionnaire

The questionnaire was similar to those used in previous surveys among medical students [ 22 ] and future teachers [ 21 ] and included scales on mental health (sense of coherence, psychological distress, perceived stress, depression, social support; the scales for perceived stress and depression were not included in the questionnaire for public health students), perceived health, health locus of control (how much can do for own health), demographic (age, sex, residence) and socioeconomic data (parents’ educational level, family’s economic status). Items not referred separately were taken from the tool of the Hungarian National Health Interview Survey (HNHIS) of 2003 [ 23 ]. The full questionnaire took around 15–20 min to fill.

Sense of coherence was measured by the abbreviated 13-item scale (SoC-13) [ 24 ] which had been validated in Hungarian [ 25 ]. Items were answered on a 7-point Likert scale; the total score varied between 13 and 91. Higher scores indicate greater sense of coherence.

The 12-item version of the General Health Questionnaire (GHQ-12) was used to detect psychological distress. Questions were answered on a 4-point Likert scale. Cases were detected by scoring in the simplest manner [ 26 ] which assigns a score of 1 to each symptom, while lack of a particular symptom is scored by 0 so that the total score varies between 0 and 12. Total score above 4 was set as the threshold indicating notable psychological distress, identical to that used in the Hungarian National Health Interview Survey of 2003 [ 27 ].

The 9-item Hungarian version of the Beck Depression Inventory (BDI) was used to assess depression [ 28 ]. Scores below 9 indicated no depression; scores between 10 to 18 indicated mild depression; scores between 19–25 indicated moderate depression; scores above 25 estimated severe depression.

The questionnaire of the mental health survey of the general population included demographic items as well as the SoC-13 and the GHQ-12 scales.

2.3. Statistical Methods

Data were entered into a Microsoft Excel database. Data entry as well as the full database were checked and cleaned by removing inconsistent answers. Data from all three student groups (public health, nursery education, physiotherapy) were merged into one database and analyzed together. Intercooled Stata 10.0 for Windows was used for data transformation and analysis. Categorical variables were analyzed by the chi-squared test and Fisher’s exact test. Results were compared to that of a representative survey of the Hungarian adult population using the two-sample test of proportion.

Available case analysis was used since less than 5% of the data were missing for all variables, and data were assumed to be missing completely at random based upon the result of Little’s test ( p = 0.401). Sensitivity analysis was not performed for handling of missing data.

3.1. Demographic and Socioeconomic Data

A total of 412 of the potentially eligible 515 students were present at the time of the data collection and all of them agreed to participate in the study. Three records were deleted after data checking because less than one-quarter of the questions were filled out. The overall response rate was 79.4% ( n = 409). Mean age of the sample was 20.81 years (SD: 1.99; min. 18, max. 41); the majority (98.5%) being under 26 years of age. All three courses were dominated by females, but their proportion was significantly lower among public health students (83.5%) compared to the two other groups (students of nursery education, 96.9%; physiotherapy, 93.1%; p < 0.05). The sex ratio was representative of the students at these courses. Respondents were representative by study year for each course. Detailed demographic and socioeconomic data are shown in Table 1 .

Demographic and socioeconomic data of the participants.

Features of the total sample are marked with bold.

3.2. Creating a Composite Indicator for Assessment

The basic concept of the assessment tool was that it should assess both the positive and the negative aspects of mental health in a non-stigmatizing manner. GHQ has been widely used as a screening device for identifying minor psychiatric disorders in the general population, but it reflects a deficit-based approach to mental health. Antonovsky’s salutogenic concept is oriented to resilience, so combining the two tools provides a more balanced assessment of mental status.

A composite indicator of mental well-being was created from sense of coherence ( n = 399) and psychological distress ( n = 402) to assess mental health. For SoC, there is no threshold below which it could be considered too low or not normal, therefore—in line with previous studies [ 29 , 30 , 31 , 32 , 33 ]—after defining tertiles of the total score, SoC scores at the highest tertile (above 65 points) were considered normal, whereas scores in the two other tertiles as low. As to psychological distress, the cut-off value of 5 points was used to identify those who were not distressed. A composite indicator for mental status was created from the categories of SoC and GHQ using a 2 × 2 table with the following categories: (1) resilient (normal SoC, normal GHQ); (2) vulnerable (low SoC, high GHQ); (3) non-classifiable: normal SoC and high GHQ; or (4) non-classifiable: low SoC with normal GHQ ( Table 2 ).

Creation of a composite indicator for assessing mental status in students.

In order to check the predictive ability of our assessment tool, positive (LR+) and negative (LR−) likelihood ratios were calculated based on the Bayes theorem considering the BDI score and perceived health as reference. The BDI-9, SoC-13 and GHQ-12 scores were available for 256 students; perceived health, SoC-13 and GHQ-12 were known for 393 students. BDI categories reflecting moderate and severe depression (over 18 points) were combined to identify those at-risk (mentally vulnerable); those who scored below 19 points were defined not at risk after Kopp and her co-workers [ 28 ]. Perceived health was dichotomized into categories of having good or very good vs. fair or less than fair perceived health. LR was used because it does not require dichotomization so all four categories (including the two non-classifiable risk categories with conflicting SoC and GHQ results, Table 2 ) were used to calculate the LR. Nonparametric ROC analysis was performed to calculate the area under the curve (AUC).

3.3. Mental Status of the Students Assessed with a New Composite Indicator

The assessment capacity of the composite indicator created from sense of coherence and psychological distress was tested as described above. Based on the assessment tool, four groups of students could be identified, as shown in Table 3 , of which two groups showed congruent results according to GHQ-12 and SoC-13 alike. More than one-quarter (28.4%, 95% CI 24.2% to 33.1%) of students could be defined as being resilient, that is, having good mental health with normal sense of coherence and low distress. Vulnerable students had low sense of coherence along with high levels of distress, comprising one-quarter (25.1%, 95% CI 21.1% to 29.7%) of the participants. Two other groups with inconsistent results captured 46.5% of the students: 44.2% of them had low sense of coherence and normal levels of distress; 2.3% had normal sense of coherence and high levels of distress. There was a significant gender difference in resilience (normal SoC, normal GHQ) with almost twice as many resilient males (43.2%, 95% CI 28.2% to 59.6%) compared to females (27.0%, 95% CI 22.7% to 31.9%; p = 0.038).

Mental well-being of students assessed with the composite indicator.

3.4. Mental Status of the Students Compared to the General Population

The proportion of vulnerable students (low SoC and high GHQ) was compared to 18- to 25-year-olds in the general population and was found to be three times higher (25.1% vs. 7.9%; p < 0.001) ( Table 4 ). There was no significant gender difference regarding the proportion of those at risk in the general population (males: 9.8%, females 6.4%; p = 0.559) and there was also no difference between at-risk male students and their peers in the general population (13.5% vs. 9.8%; p = 0.605). The proportion of vulnerable female students was nearly four times higher compared to their peers in the general population (26.2% vs. 6.4%; p = 0.003).

Mental well-being of 18–25-year-old Hungarian adults assessed with the composite indicator.

3.5. Predictive Ability of the Composite Indicator

The LR+ of the composite indicator was 2.61, meaning that the prevalence of vulnerable mental status was 2.61 times higher among depressed students compared to students who were not depressed (positive post-test probability = 0.405). LR− was 0.054 producing a negative post-test probability of 0.014. Accordingly, students who scored depressed by the BDI would be 18 times less frequently categorized as resilient by the composite index compared to those without symptoms of depression. The positive predictive value of the assessment tool calculated with the above data proved to be 40.5% as opposed to the negative predictive value of 98.6%. The LR for those who had low SoC and normal stress was 1.05 showing that this combination is more frequent among depressed students. The AUC was 0.743 (95% CI 0.683–0.803).

The predictive ability of the new assessment tool against BDI was compared with the predictive ability of the GHQ and SoC alone. For GHQ-12, the positive predictive value was 36.6% while the negative predictive value was 86.8%. For SoC-13, the positive predictive value was 29.6% and the negative predictive value was 98.8%.

Among students with bad subjective health, the prevalence of vulnerable mental status was 2.6 times higher compared to students who were in good health (LR+ = 2.60, positive post-test probability was 0.606). LR− was 0.32 producing a negative post-test probability of 0.161. This shows that students in bad subjective health would be 3 times less frequently categorized as resilient by the composite index compared to those with good health. The positive predictive value of the assessment tool calculated with the above data proved to be 60.6% as opposed to the negative predictive value of 83.9%. The LR for those who had low SoC and normal stress was 1.02 showing that this combination is more frequent among students with bad health. The AUC was 0.687 (95% CI 0.637–0.736).

The predictive ability of the new assessment tool against bad subjective health was compared to the predictive ability of the GHQ and SoC alone. In case of GHQ, the positive predictive value was 58.3%, while the negative predictive value was 70.9%. For SoC, the positive predictive value was 45.9% and the negative predictive value was 82.6%.

4. Discussion

Using the new assessment tool, nearly 30% of students were identified as resilient with normal SoC and low GHQ. Mental health was considered worrisome for those who had low SoC and high GHQ. This vulnerable category captured almost one-quarter of the study population, which is in line with another study where the proportion of those who had any mental health problem was similar (33.8% [ 34 ]).

Students with normal sense of coherence and low levels of mental distress can be considered mentally resilient according to our assessment tool. Of all resilient students, 98.6% were identified as not depressed by the Beck Depression Inventory. Of those who were defined mentally vulnerable (with low sense of coherence and high levels of mental distress), only 40.5% scored as depressed by the BDI. There was not one student in the high BDI score group who would have had high SoC and high stress, pointing to the protective effect of higher levels of SoC in relation to depression even in distressed people. Of the resilient students, 83.9% perceived their health as good.

Our assessment tool can reliably distinguish between students who are in reasonably good mental health or can be considered resilient, and those who are at increased risk and need further attention and targeted support during their studies. Our assessment tool can be reliably used to identify vulnerable students in a reasonably simple and non-stigmatizing manner that allows the provision of timely and targeted support and help during their studies.

4.1. Strengths and Limitations

An advantage of the present survey was its wide scope (inclusion of all students of three courses) and high response rate that was representative of the students by sex and study year.

The timing of data collection on mental health is critical among university students because their stress level strongly fluctuates during the academic year. In order to avoid measuring further increased stress before or during the exam period, data were collected in mid-semester when stress related to the examination period is at its lowest.

A considerable strength of our assessment tool is that it approaches mental status not only from a negative (deficit) but also from a positive (resource) perspective. The salience of our approach is reflected by identifying only one depressed student among those who had a normal sense of coherence (1.3%).

Our assessment tool has a low negative post-test probability, that is, high negative predictive value. However, predictive ability is limited by the fact that calculations were based on a reference test (BDI) with screening rather than diagnostic features. Nevertheless, BDI demonstrates a high internal consistency, with alpha coefficients of 0.86 and 0.81 for psychiatric and non-psychiatric populations, respectively [ 35 ]. The predictive ability of this assessment tool is more favorable compared to using the GHQ or SoC alone.

4.2. Mental Health Screening in Practice

The new assessment tool measuring sense of coherence and psychological distress could be offered to students in an anonymous manner, followed by evaluation and individualized online messages on recommended services and support options. Compared to the validity of other similar screening tests, the features of our assessment test are acceptable, especially if compared to the depression screening tool (BDI). The screening for depression in adults over 18 years of age is recommended by the US Preventive Services Task Force [ 36 ]. However, the positive predictive value of BDI was 54% and the negative predictive value was 99% in a study in which the prevalence of major depression was similar to that in our study [ 37 ]. Furthermore, comparing our results to a previous study where GHQ-12 alone was used to detect depression, both the positive (40.5% vs. 27.8%) and the negative (98.6% vs. 97.1%) predictive values of our assessment tool were higher [ 38 ].

Time alone does not seem to solve mental problems in college students as they tend to persist even after 2 years of follow-up [ 39 ]. There have been initiatives for reaching out to college students with mental problems, but these focused on depression [ 40 , 41 ] and the prevention of suicide [ 42 ].

In another type of initiative aiming at increasing the uptake of clinical services, the University of Washington developed a web-based system for students to self-screen for anxiety, depression, attention-deficit/hyperactivity disorder, alcohol use and eating disorder. The system logged more than 2700 visits, 1003 screening sessions and 438 referral requests in 17 months showing that such a system can increase care-seeking. However, the system requires an elaborate IT background with secure data repositories and processes of data exchange that must be supported by a sufficiently staffed primary care center capable of handling all incoming referrals [ 43 ].

Currently there is no recommendation for the screening of vulnerable students in higher education. Screening only for depression among students would miss many in need of help, such as those struggling with anxiety or substance abuse [ 7 ].

5. Conclusions

Since mental health problems are common among those in helping professions, and mental health problems burden students disproportionately more than their peers [ 22 , 44 , 45 ], the best time to take action seems to be during their years in college. Considering the help-avoiding attitude of students [ 46 ] and the perennial lack of resources to deal with mental problems at almost all institutes of higher education, we propose the assessment of mental health of students using the GHQ-12 and SoC-13 scales combined as described above. The tool has the advantage of having a balanced focus on mental health from an aspect of vulnerability as well as of resilience; it reliably separates those who are psychologically definitely at risk from those who can be considered reasonably resilient. Moreover, it is simple to use and avoids stigmatization. The test could be easily adapted to an online format, and based on its results, respondents could get detailed personalized advice on available supportive services, tailoring the message to their level of risk. A computerized risk assessment followed by personalized message would yield immediate help and might facilitate the uptake of preventive services while avoiding stigmatization and the overburdening of preventive services. An online mental health support system can be a viable alternative or supplement to university counselling services [ 47 , 48 ]. Routine administration of our assessment test would enable the monitoring of mental problems among college students and enhance seeking help, diagnosis and treatment as recommended [ 7 ].

Acknowledgments

The authors wish to thank Ágnes Nagy and Zsanett Sipos, master’s students of health promotion at the Faculty of Public Health who carried out data collection among the physiotherapy students; Lajos Olvasztó, a public health student at the Faculty of Public Health who carried out data collection among nursery education students; Laura Eszter Jenei and Andrea Bettina Siket, public health students at the Faculty of Public Health who carried out data collection among students of public health; and Zoltán Vokó for his useful methodological comments.

Author Contributions

Conceptualization, K.K. and É.B.; methodology, É.B. and K.K.; formal analysis, É.B. and K.K.; project administration, É.B.; visualization, É.B. and K.K.; writing—original draft preparation, É.B.; writing—review and editing, K.K. and R.Á.; supervision, R.Á.; funding acquisition, R.Á.

For data collection, funding was provided by the NKFP1-00003/2005 project of the Ministry of Education, Hungary. Data evaluation was carried out in the framework of GINOP-2.3.2-15-2016-00005 project. This project is co-financed by the European Union under the European Regional Development Fund. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Conflicts of Interest

The authors declare no conflict of interest.

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Social Media Use Is Linked to Brain Changes in Teens, Research Finds

Teens who frequently checked social media showed an increasing sensitivity to peer feedback, although the cause of the changes was not clear.

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By Ellen Barry

The effect of social media use on children is a fraught area of research, as parents and policymakers try to ascertain the results of a vast experiment already in full swing. Successive studies have added pieces to the puzzle, fleshing out the implications of a nearly constant stream of virtual interactions beginning in childhood.

A new study by neuroscientists at the University of North Carolina tries something new, conducting successive brain scans of middle schoolers between the ages of 12 and 15, a period of especially rapid brain development.

The researchers found that children who habitually checked their social media feeds at around age 12 showed a distinct trajectory, with their sensitivity to social rewards from peers heightening over time. Teenagers with less engagement in social media followed the opposite path, with a declining interest in social rewards.

The study , published on Tuesday in JAMA Pediatrics, is among the first attempts to capture changes to brain function correlated with social media use over a period of years.

The study has important limitations, the authors acknowledge. Because adolescence is a period of expanding social relationships, the brain differences could reflect a natural pivot toward peers, which could be driving more frequent social media use.

“We can’t make causal claims that social media is changing the brain,” said Eva H. Telzer, an associate professor of psychology and neuroscience at the University of North Carolina, Chapel Hill, and one of the authors of the study.

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What research actually says about social media and kids’ health

There is no clear scientific evidence that social media is causing mental health issues among young people. Here’s what we do know.

There is no clear scientific evidence that social media is causing mental health issues among young people. Public health officials are pushing for regulation anyway.

U.S. Surgeon General Vivek H. Murthy on Monday called for social media platforms to add warnings reminding parents and kids that the apps might not be safe, citing rising rates of mental health problems among children and teens. It follows an advisory Murthy issued last year about the health threat of loneliness for Americans, in which he named social media as a potential driver of social isolation.

But experts — from leading psychologists to free speech advocates — have repeatedly called into question the idea that time on social media like TikTok, Instagram and Snapchat leads directly to poor mental health. The debate is nuanced, they say, and it’s too early to make sweeping statements about kids and social media.

Here’s what we do know about children and teens, social media apps and mental health.

Why it’s hard to get a straight answer

There is evidence that adverse mental health symptoms among kids and teens have risen sharply, beginning during the global financial crisis in 2007 and skyrocketing at the beginning of the pandemic. But research into social media’s role has produced conflicting takeaways.

While many studies have found that social media use is correlated with dips in well-being , many others have found the opposite . One problem may be that terms such as “social media use” and “mental health” have been defined broadly and inconsistently, according to analyses of existing studies . Whatever the reason, it’s challenging for researchers to find causal relationships (meaning A causes B) between social media and mental health without closely controlling children’s behavior.

That’s hasn’t stopped health organizations from issuing warnings, such as a 2011 statement from the American Academy of Pediatrics Council on Communications and Media urging parents to look out for “Facebook depression.” A 2013 study suggested such warnings were “premature.”

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To help answer the question, “How does social media impact kids?” researchers need more robust data.

In a Monday opinion essay in the New York Times , Murthy also called for social media companies to share data and research into health effects so independent experts can examine it. “While the platforms claim they are making their products safer, Americans need more than words. We need proof,” he wrote.

Vulnerable kids are more likely to struggle

Sometimes, social media appears to boost anxiety and depression. Other times, it appears to boost well-being and connectedness, according to a 2022 analysis of 226 studies .

So when we ask whether social media is a community hub for LGBTQ+ youths or a rabbit hole of warped information, the answer can be “both.” Bigger factors may be a teen’s existing vulnerabilities and what they’re actually doing on social media apps, American Psychological Association Chief Science Officer Mitchell Prinstein has said .

Some studies have found that kids and teens who already struggle with their mental or emotional health are more likely to come away from social media feeling anxious or depressed. It’s hard to determine whether social media is causing depressive symptoms. One 2018 study found that while time on social media didn’t correlate with depression, young women with depression tended to spend more time on the apps.

It’s not clear why social media might affect mental health

Social media leaves some people feeling bad , some studies suggest , but scientists still don’t understand why.

David Yeager, a developmental psychologist at the University of Texas at Austin, said some possible contenders are social comparison, where we weigh our own life next to another person’s. Or maybe it’s guilt, where we feel lazy or unproductive after spending time scrolling. Of course, disappointment and guilt are age-old feelings, but social media may provoke them, Yeager said.

Social media isn’t the first new technology to raise concerns. A newspaper clipping from 1882 shows an author claiming the telephone was “an aggravation of so monstrous a character as to merit public denunciation.” People in the 1920s were worried that the radio would make people stop socializing in person.

Instead of fighting about whether social media is good or bad, it’s more important to figure out how to minimize the harm of social media’s negative elements and maximize the benefit of its good ones, Yeager said.

“Our technology has changed, but human nature hasn’t,” he said. “The things that drive us, compel us and trap us are still the same.”

Social media companies design products to keep us scrolling

Like all businesses, social media companies exist to make money. That means creating experiences to keep users scrolling on their apps — and viewing advertisements.

One way they accomplish that is by gaming our attention or emotions. Washington Post reporting has shown, for instance, that Facebook’s algorithm at one point weighed the anger reaction more strongly than a “like” because outrage tended to create more engagement.

“Rather than scaring kids and parents with half-truths, we should demand policies that force companies to end harmful business practices like surveillance advertising and manipulative design features,” said Evan Greer, director at the digital rights nonprofit Fight for the Future. Surgeon General Murthy called for similar measures in his Times essay.

Why some people are playing up (or downplaying) risks and worries

Most experts call for a measured approach to discussing social media’s potential health impacts, but not all. For example, social scientist Jonathan Haidt recently published “The Anxious Generation,” a book that attributes poor mental health among teens to social media. In it, Haidt calls for parents to keep kids off the apps before high school and off smartphones altogether until age 16. Other researchers, including University of California Irvine psychologist Candice Odgers, have said the book misinterpreted existing studies to fuel a moral panic.

“This book is going to sell a lot of copies, because Jonathan Haidt is telling a scary story about children’s development that many parents are primed to believe,” Odgers wrote in an essay for Nature . Some of Haidt’s readers, meanwhile, celebrated what felt like direct acknowledgment of a difficult problem.

Future research may come at this contested question from new directions. An article published in Nature last month, for instance, recommended researchers consider how changes to behavior and cognition during adolescence might interact with social media and put mental health at risk.

Taylor Lorenz contributed to this report.

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