types of qualitative research historical analysis

Qualitative study design: Historical

• Qualitative study design
• Phenomenology
• Grounded theory
• Ethnography
• Narrative inquiry
• Action research
• Case Studies
• Field research
• Focus groups
• Observation
• Surveys & questionnaires
• Study Designs Home

Looking at the past to inform the future.

Describing and examining past events to better understand the present and to anticipate potential effects on the future. To identify a need for knowledge that requires a historical investigation. Piecing together a history, particularly when there are no people living to tell their story.  

• Oral recordings

Can provide a fuller picture of the scope of the research as it covers a wider range of sources. As an example, documents such as diaries, oral histories and official records and newspaper reports were used to identify a scurvy and smallpox epidemic among Klondike gold rushers (Highet p3).

Unobtrusiveness of this research method.

Limitations

Issues with validity – can only use the historical information that is available today.

Primary sources are hard to locate.  

Hard to triangulate findings (find other resources to back up the information provided in the original resource). 

Example questions

• What caused an outbreak of polio in the past that may contribute to the outbreaks of today? 
• How has the attitude to LGBTQIA+ changed over the past 50 years?

Example studies

• Hallett, C. E., Madsen, W., Pateman, B., & Bradshaw, J. (2012). " Time enough! Or not enough time!" An oral history investigation of some British and Australian community nurses' responses to demands for "efficiency" in health care, 1960-2000 . Nursing History Review, 20, 136-161. 
• Navarro, J. A., Kohl, K. S., Cetron, M. S., & Markel, H. (2016). A tale of many cities: a contemporary historical study of the implementation of school closures during the 2009 pA(H1N1) influenza pandemic. Journal of Health Politics, Policy and Law, 41(3), 393-422. Retrieved from  http://ezproxy.deakin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,sso&db=lhh&AN=20163261834&site=ehost-live&scope=site   

Edith Cowan University Library. (2019). Historical Research Method. Retrieved from  https://ecu.au.libguides.com/historical-research-method   

Godshall, M. (2016). Fast facts for evidence-based practice in nursing: Implementing EBP in a nutshell (2nd ed.). New York: Springer Publishing Company. 

Highet, M. J. (2010). "It Depends on Where You Look": The Unusual Presentation of Scurvy and  Smallpox Among Klondike Gold Rushers as Revealed Through Qualitative Data Sources. Past Imperfect, 16, 3-34. doi:10.21971/P7J59D 

Saks, M., & Allsop, J. (2012). Researching health: qualitative, quantitative and mixed methods (2nd ed.). London: SAGE. 

Taylor, B. J., & Francis, K. (2013). Qualitative research in the health sciences: methodologies, methods and processes. Abingdon, Oxon: Routledge. 

University of Missouri-St. Louis. Qualitative Research Designs. Retrieved from http://www.umsl.edu/~lindquists/qualdsgn.html   

• << Previous: Ethnography
• Next: Narrative inquiry >>
• Last Updated: Jun 13, 2024 10:34 AM
• URL: https://deakin.libguides.com/qualitative-study-designs

Qualitative Research: An Introduction to Methods and Designs by

Get full access to Qualitative Research: An Introduction to Methods and Designs and 60K+ other titles, with a free 10-day trial of O'Reilly.

There are also live events, courses curated by job role, and more.

The Why: Historical Interpretation and Analysis

Rather than just telling a story, although sometimes historians do some very good storytelling, historical research is grounded in the analysis and interpretation of the past (see Chapter Nine , Narrative Inquiry, for another perspective on stories in research). Analysis and interpretation move historical research from being a chronicle of events to providing a larger understanding of why things were as they were in the past. History tells you about the past and why the past was as it was. That is the subjective part of historical research. Certainly, picking topics, determining the scope and foci of a study, and analyzing documents are all subjective because they rely on the historian's decisions ...

Get Qualitative Research: An Introduction to Methods and Designs now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Don’t leave empty-handed

Get Mark Richards’s Software Architecture Patterns ebook to better understand how to design components—and how they should interact.

It’s yours, free.

Check it out now on O’Reilly

Dive in for free with a 10-day trial of the O’Reilly learning platform—then explore all the other resources our members count on to build skills and solve problems every day.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

HISTORICAL RESEARCH: A QUALITATIVE RESEARCH METHOD

This paper is a write-up about one of many qualitative research method, namely historical research method.

Related Papers

International Journal for Research in Applied Science & Engineering Technology (IJRASET)

IJRASET Publication

Historical research describes the past things what was happened. This is related with investigating, recording as well as interpreting the past events with respect to the in present perspectives. Historical research is a procedure for the observation with which researcher. It is a systematic collection and objective evaluation of the collected data with respect to the first occurrence to verify causes and effects related to the events with the help of these two explain the present events as well as anticipate for the future work purpose

Scholarly R E S E A R C H Journal

To find out how and why theories and practices have been developed which are now prevail in schools , a study on the purpose of historical research is very helpful. It deals with the significance of education and its interrel ationship with school and curriculum. In the said research, a study of Historical Research was conducted. Keywords - Historical, Perspective, Predictions, Facts, Past, Hypothesis

ICERI2009 Proceedings

Costas Vassilakis

The paper presents the results of a study on how historians conduct research in a historical archive, and the methodologies they use while searching. Historic research involves finding, using and correlating information within primary and secondary sources, in order to understand past events. Historians conduct research systematically, by examining past events to renew the past; historic research may involve interpretation to recapture the nuances, personalities, and ideas that have influenced these events, and the expected ...

Methods of Analysis

Stephen Petrina

It would seem that historical method has always implied case study if interpreted as the history of single events, episodic history as different from universal history, courtes durées as different from longues durées. From the early twentieth century, historical case study was basically biography, particularities of individuals used to counter the “vast amount of generalization” marking most histories and textbooks (Nichols, 1927, p. 270). Yet historical case study, in the way historians think of it, is primarily a post-WWII methodology.

Miguel S. Valles

El uso de materiales de archivo como punto de partida al disenar y acometer la investigacion social da por supuesto que en nuestras sociedades complejas ha echado raices hace tiempo una cultura de archivo (para compartir y reusar). Esta mentalidad y practica de investigacion se ha desarrollado primero y esta bien asentada en el caso de las estadisticas, encuestas y otros documentos primarios o secundarios. En cambio, es menos frecuente y ciertamente no una actividad rutinaria por lo que respecta a los datos cualitativos. Unicamente algunos de los materiales primarios y elaborados que se reunen durante la investigacion cualitativa pasan a formar parte de un archivo para su ulterior re-analisis. Pueden ser practicas y experiencias de trastienda de un proyecto, materiales en bruto tales como notas de campo, grabaciones de audio y visuales, y otros documentos producidos a lo largo del proceso de investigacion. Este volumen presenta una variopinta gama de articulos que abordan experienci...

Joseph Bryant

The Research on History I

ISTES Publication

The Research on History I Editors Özlem Muraz Budak Through its wide field of study, the science of history has succeeded in addressing all kinds of issues that have happened in the past. Many events that have happened in the past and affect the future have found a place in the science of history. It is important for every nation to know its history in order to learn lessons from the past. Every nation has a unique culture and these cultures extend to the present day. The science of history is used to learn about the past. This book aims to contribute to the development of scientific publications and publishing in social sciences in general and history in particular. In this sense, qualified studies covering every subject related to both national and regional history and world history are included. We will be pleased to contribute to the literature with these original works written in every field of history and to the qualified scientific studies related to the auxiliary branches of history. Citation Budak, Ö. M. (Ed.). (2023). The Research on History I. ISTES Organization.

Albert Mills

Forum Qualitative Sozialforschung/Forum: Qualitative Social Research

Maria Tamboukou

The use of archival materials as a point of departure when designing and launching social research takes for granted that a culture of archiving (for sharing and re-use) has rooted time ago in our complex societies. This mentality and research practice first flourished and is fairly well installed in the case of statistics, surveys and certain other primary or secondary documents. On the contrary, it is less frequent and certainly not a routine activity for qualitative data. Only some of the raw and elaborated materials gathered during ...

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Denise Nicole Green

Isidora Sáez-Rosenkranz

Jim Parsons

American Political Science Review

Flemming Mikkelsen

William Outhwaite and Stephen P. Turner, eds., The SAGE Handbook of Social Science Methodology. (London: SAGE.

John R. Hall

Anil Kumar Prasanna Devaramatha Magala

Linda Chiang

Robert Rosenstone

Journal of Management History

Wim van Lent

Arie M Dubnov

AJHSSR Journal

Approaches to Historiography II.

Vilmos Erős

Business & Society

Lyn Batchelor

Daniel A Starrs

Bento Selau , Fabiane Adela Tonetto Costas , Manuel de la Mata , Malba Barahona , Manolis Dafermos , Mercedes Cubero , ricardo baquero , Julia Lucas , Alyona Ivanova

lorenzo bosi

purnawan basundoro

Library &amp; Information Science Research

Michael Widdersheim

Histos Supplement 15

Andrew G Scott

American Communist History

Dermot Quinn

Thomas Cauvin

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

Prevent plagiarism. Run a free check.

Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved June 26, 2024, from https://www.scribbr.com/methodology/qualitative-research/

Is this article helpful?

Pritha Bhandari

Other students also liked, qualitative vs. quantitative research | differences, examples & methods, how to do thematic analysis | step-by-step guide & examples, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Doc’s Things and Stuff

Section 6.1: Qualitative and Historical Research

When we talk about research, we’re really talking about a way to answer questions. You’ve probably heard about experiments, surveys, and maybe even something called “quantitative research.” But there’s another type that’s super important, and it’s called “qualitative research.”

Table of Contents

What is Qualitative Research?

So, what’s the deal with qualitative research? It’s a way for researchers to explore and understand the meaning behind certain behaviors, emotions, and interactions. Unlike quantitative research, which focuses on numbers and statistics (like how many people prefer video games to sports), qualitative research is all about descriptions and experiences (like why people prefer video games to sports).

Some people might think that qualitative research is not as serious or tough as quantitative research, but that’s not true! It’s just as challenging and valuable—it’s just looking for different types of answers to different types of questions.

Why Some People Get Confused

You might wonder why there’s confusion about qualitative research. Well, it’s often because it’s not about counting or measuring things in a traditional way. Some researchers who love numbers might not see the full value of studying people’s stories or experiences. They might see it as a “catchall” category, which is a bit unfair.

How is Historical Research Different?

Historical research is like a cousin to qualitative research. It has been around for ages and has its own way of looking at the past to answer questions. Some might argue that it’s a special kind of qualitative research because it also looks at stories and not just numbers.

The Mindset of Researchers

Imagine a researcher as a detective. A quantitative researcher is like a detective who has a specific idea of who the culprit in a mystery might be. They’re trying to see if the clues match their guess. They start with a theory and then look at the real world to see if things add up.

On the other hand, a qualitative researcher is like a detective who comes to the scene with an open mind. They look around, gather information, and let the clues lead them to conclusions.

So, both types of research are like detectives working in different ways to solve mysteries—quantitative researchers test their guesses against the world, and qualitative researchers let the world reveal its own story.

Historical Research

Historical research isn’t just about memorizing dates and names. It’s like being a time detective, trying to really understand what happened in the past and why. Good historical research digs deep—it’s about figuring out how past societies worked and what made people tick.

Theories and Evidence in History

Historians are a bit like scientists. They have theories and hypotheses about the past. They use evidence, like letters, photos, and other records, to build their cases and test their ideas. It’s not all about storytelling; there’s a lot of careful thinking and analyzing that goes into it.

Why Historical Research Matters

Think about the issues we face today, like the relationships between communities and the police. By looking back at events like the Civil Rights Movement, we can learn lessons that help us understand and maybe even solve problems today. History isn’t just old news—it’s a guide to the present and the future.

Steps of the Historical Detective

The way historians work isn’t too different from other researchers. They start by pinpointing what they want to find out. Then they form a hypothesis—a smart guess about their question. After that, it’s all about gathering data, analyzing it, and seeing if their guess holds up. They’re not just repeating what’s already known; they’re uncovering new insights and truths.

The Challenge of Being Objective

Here’s a tricky part: Historians have to be really careful not to let their personal feelings color their work. They need to look at all the evidence, even if it goes against what they believe or feel. Their goal is to find the most truthful explanation of the past, not just to tell a compelling story.

The Limitations of Historical Data

Historians face a unique challenge—they can’t make new data. They’re stuck with whatever evidence has survived over time. This can make some questions hard, or even impossible, to answer. If a student is researching history, they need to be careful not to bite off more than they can chew and to make sure there’s enough evidence out there.

The Hunt for Information

Unlike other researchers, historians don’t just look at recent journal articles. They dive into all kinds of records—diaries, letters, old receipts, you name it. And sometimes, they have to go on real-life adventures, traveling to far-off places just to find that one piece of the puzzle.

The Power of Primary Sources

In historical research, firsthand accounts are like gold. These primary sources are the most direct peek into the past we can get. Secondary sources, like textbooks, are useful, but they’re a step removed from the action. Historians always aim for those primary sources first for the most accurate picture.

Verifying the Facts

Historians also have to be super detectives when it comes to figuring out if a document is legit. They ask: Was the author really there? How long after the event was this written? And, big one, was the author biased? All these questions help them determine if their sources are trustworthy.

Crafting the Story

Finally, historians have to take all this data and tell the story of the past in a way that’s true to what they’ve found. It’s not about creating a nice tale; it’s about making sense of the evidence and sharing those discoveries with others. They take all those facts and build a narrative that teaches us something new.

🔍 Reflect: Why do you think it’s crucial for historians to be objective in their research? How might their personal feelings or biases affect the way they interpret historical data?

Go to top      

Qualitative Research

Narrative data: the storytelling of science.

Qualitative research is the art of uncovering the rich, complex stories behind human behavior and social phenomena. Instead of crunching numbers, qualitative researchers listen to people’s stories and observe their behaviors in real life—right where they happen.

Natural Settings: The Real-World Laboratory

The world is the qualitative researcher’s lab. They dive into the natural environments where life’s drama unfolds, rather than observing from the artificial confines of a lab. This hands-on approach is why some people use terms like ‘field research’ or ‘naturalistic research’ interchangeably with qualitative research.

Culture Explorers: The Ethnographers

In their quest to understand cultures, qualitative researchers often wear the hat of ethnographers, immersing themselves in the day-to-day lives of the people they study. Depending on who’s doing the research, you might hear different names for this work, but they’re all about getting to the heart of human experiences.

Shifting Tides: From Numbers to Narratives

Once upon a time, social scientists wanted to be just like the “hard science” folks—measuring everything with numbers to keep it objective. But there’s been a revolution. More and more researchers argue that the rich tapestry of human life can’t be captured by numbers alone. These scholars are making a strong case for the power of words and observations to fill in the picture.

The Holistic Approach: Seeing the Big Picture

For qualitative researchers, it’s all about the big picture. They believe you can’t understand human behavior by looking at pieces in isolation. Instead, they see social events as part of larger systems—like a giant puzzle they’re trying to solve, piece by piece.

Building Theories: The Creative Side of Research

While numbers are great for testing theories, stories and observations are where new theories begin. Qualitative research is a breeding ground for new ideas about how societies work because it takes a deep dive into the complex ways people interact with each other and their environments.

Tools of the Trade: The Qualitative Toolkit

To get the full story, qualitative researchers have a whole toolbox of methods at their disposal. They might join the community they’re studying, watching and learning from the inside. They might have long, detailed chats with people to get their perspectives. And they often dig into letters, photos, and any other documents that can give them insights.

🔍 Reflect: Bold What are some challenges you think qualitative researchers might face when trying to maintain objectivity? How might their presence in the natural environment of their subjects influence the data they collect?

Deciphering the Logic of Qualitative Research

Deductive reasoning: the “top down” approach.

Quantitative research often follows a deductive path. This approach is like constructing a building from the blueprint down to the bricks. Researchers start with a broad theory and narrow it down to specific hypotheses, which they then test with data. It’s a logical staircase from the general principles down to specific instances.

Deductive Logic in Steps:

• Theory Specification : Select a theory as a starting point.
• Hypothesis Generation : Make predictions based on the theory.
• Data Collection : Gather evidence to test these predictions.
• Confirmation/Refutation : See if the real-world data matches the theory.

Inductive Reasoning: The “Bottom Up” Approach

Qualitative research flips the script, embracing an inductive strategy. Picture a sculptor carving a statue from a block of marble, finding the form as they go. Researchers immerse themselves in the details—specific behaviors or events—and from these observations, they extract broader patterns, categories, and ultimately theories. This method is more exploratory, more like a journey from the specific to the general .

Inductive Logic in Steps:

• Data Collection : Dive into the social world and gather observations.
• Pattern Identification : Look for recurring themes and categories.
• Theoretical Development : Formulate a general theory based on these patterns.
• Grounded Theory Formation : Develop a theory that’s rooted in observed data.

Descriptive and Theoretical Qualitative Research

While some qualitative research is content to simply paint a picture of the social landscape, other studies aim higher, seeking to construct new theories. These theories are termed grounded theory because they’re firmly planted in the reality observed by the researcher.

The Interplay of Questions and Logic

The specificity of questions a researcher asks is influenced by the research approach. Quantitative research often has very pointed, precise questions, while qualitative research deals with broader, more open-ended inquiries. The choice between these methods depends on the depth of understanding currently available about a subject.

Choosing the Path: Specificity vs. Exploration

If a research question is vast and the existing theory is like an unfinished map, qualitative research steps in to chart the unknown territories. Without enough detail for sharp hypotheses, qualitative researchers opt for a broad lens to capture more of the landscape.

🔍 Reflect: Bold Consider the research approach best suited to a study on the effects of social media on teenage communication skills. Would you start with a hypothesis based on existing theory (deductive), or would you observe and then form a theory (inductive)? How would your approach influence the depth and direction of your research?

Understanding the Synthesis of Data in Research

The quantitative data symphony.

In the realm of quantitative research, data synthesis is like a classical music score — precise, structured, and composed using the mathematical notes of descriptive statistics to organize data, and inferential statistics to confirm or reject hypotheses. The end product? A suite of neat, accessible tables summarizing a multitude of data points like a well-orchestrated symphony.

The Qualitative Data Mosaic

On the flip side, qualitative research resembles a vast mosaic. Each piece of data — or in this case, words — is like a unique tile. The researcher’s task? To arrange these tiles to form coherent patterns and themes. The process is intricate and sometimes overwhelming, leading to the inevitable question: “What do I do with all of these words?”

The Craft of Trends, Patterns, and Categories

The qualitative researcher’s strategy is akin to an artist searching for form within chaos. They meticulously look for trends (recurring themes), patterns (relationships and structures), and categories (classifications). These are the strokes that paint the bigger picture of the data narrative.

Literature Review vs. Qualitative Data Analysis

The process bears resemblance to conducting a literature review. Yet, there’s a crucial distinction — the synthesis focus . A literature review compiles scholarly writings, creating a foundation of what’s known. Qualitative data analysis, however, involves interpreting raw data to forge new understanding.

The Role of Technology

In modern qualitative research, computer software has become the researcher’s right hand. These digital tools assist in sorting, categorizing, and organizing narrative data, allowing for a more systematic approach to understanding the nuanced stories within the data.

🔍 Reflect: Imagine you’ve gathered a multitude of interviews on personal experiences during a historical event. How might software aid in finding commonalities or unique perspectives? Would the digital organization of narratives shape the insights you derive, and if so, how?

Understanding Qualitative Research

Qualitative research seeks to understand the ‘why’ behind human behaviors, experiences, and emotions, rather than focusing on ‘how many’ as seen in quantitative research. It’s a valuable, in-depth approach often misunderstood due to its descriptive nature, contrasting with the numerical focus of quantitative studies. Researchers in this field act like open-minded detectives, gathering narrative data and observing reality to develop new theories.

The Essence of Historical Research

Historical research delves into understanding past events to inform the present and future. Historians work like scientists, forming hypotheses and scrutinizing evidence like letters and photos to reveal new insights. Objectivity is crucial in historical research to avoid bias, and the reliance on surviving evidence poses unique challenges for historians who often seek out primary sources to reconstruct the past accurately.

Decoding Research Methodologies

The qualitative research methodology adopts an inductive approach, starting from specific observations and building up to general theories. This ‘bottom-up’ strategy contrasts with the ‘top-down’ deductive reasoning of quantitative research that begins with a general theory and narrows down to specific hypotheses.

The Interplay Between Qualitative and Quantitative Research

The comparison between qualitative and quantitative research highlights their distinct paths: the former is inductive, subjective, and holistic, while the latter is deductive, objective, and focused. The selection of a research method deeply influences the specificity and exploration depth of a study, with qualitative research favoring a broader lens for exploration.

Synthesizing Qualitative Data

Qualitative data analysis is likened to creating a mosaic, where each piece of narrative information is arranged to form a comprehensive pattern, revealing the overarching themes of the research. This artistic arrangement of data showcases the uniqueness of qualitative research, emphasizing context and tentative generalizations.

You are welcome to print a copy of pages from this Open Educational Resource (OER) book for your personal use. Please note that mass distribution, commercial use, or the creation of altered versions of the content for distribution are strictly prohibited. This permission is intended to support your individual learning needs while maintaining the integrity of the material.

This work is licensed under an  Open Educational Resource-Quality Master Source (OER-QMS) License .

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed .

• Harvard Library
• Research Guides
• Faculty of Arts & Sciences Libraries

Library Research Guide for Folklore and Mythology

• Theory and Methods

What is Research?

Practicing folklore.

• Research Design & Tools

Research Theories and Methods

• Ethical Guidelines in Different Disciplines
• Searching HOLLIS
• Databases & Bibliographies
• Encyclopedias
• Dictionaries
• Tale-Type and Motif Indices
• Archives & Special Collections
• Material Culture
• Audiovisual Materials & Musical Scores
• Requesting & Accessing Materials
• Organizing and Managing Your Resarch
• Learning With the Library

Research  is the systematic investigation of a subject, topic, or question. 

Data  is the information gathered during research.

Fieldwork  is the collection of data in its natural environment.

A white paper is a report or guide that synthesizes a complex topic or question and the state of information and ideas about it.

Scholarship  is, broadly, the activity of a scholar. More specifically though, the term refers to the writings of scholars which result from their research. The scholarship of a field or discipline are the books, articles, etc. which have been written on the field or discipline, or on a specific subject, topic, or question in the field or discipline.  

What is a theory?

A  theory  is the conceptual basis of a subject or area of study. It is the ideas which underlie how something is understood and the framework within which it is studied.  

What is a method?

A  method  is the process or tool used to collect data.

There are three method types: qualitative, quantitative, and historical. Likewise, some research uses mixed methods.

Qualitative research  is interested in the specific. It studies things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them, endeavoring to understand human behavior from the perspective of the individual.

Qualitative methods  collect data through observation. Qualitative methods include text analysis, interviews, focus groups, observation, record keeping, ethnographic research, case study research.

Qualitative data is descriptive. Qualitative data cannot be precisely measured and is, rather, analyzed for patterns and themes using coding. Qualitative data includes narratives, recordings, photographs, oral histories, etc.

Quantitative research  is interested in the general. It studies general laws of behavior and phenomena across different settings and contexts. This type of research endeavors to form conclusions about social phenomena, collecting data to test a theory and ultimately support or reject it.

Quantitative methods  collect data through measuring. Quantitative methods include experiments, surveys, questionnaires, statistical modeling, social networks, and demography.

Quantitative data  is numerical and statistical. It is data that can either be counted or compared on a numeric scale. Quantitative data includes statistical information. 

Historical research  is interested in the past. It reviews and interprets existing data to describe, explain, and understand past actions or events.

Historical methods  collect and analyze existing data and analyze it. Historical methods include text analysis, cultural analysis, visual analysis, archival research.

Historical data  is data which was created in the past. Historical data includes scholarship, records, artifacts.  

A methodology  is the rationale for the research approach and the methods used. It is based upon the theories underlying the field or discipline of the research.

Library of Congress YouTube Feed: Folklore

The American Folklife Center at the Library of Congress produces videos about the practice of folklore, featuring interviews with a variety of folklorists about their careers, methods, fieldwork experiences, and the implications and applications of their work.

Research Design: Qualitative, Quantitative, and Mixed Methods Approaches

John W. Creswell 2014, fourth edition

Research Design: Quantitative, Qualitative, Mixed Methods, Arts-Based, and Community-Based Participatory Research Approaches

Patricia Leavy 2017

• Literatures
• Linguistics
• Anthropology
• Human Geography

Cultural Studies

Folklore studies, literary studies.

Literary Studies, also called Literary Criticism, is the study of the written works of cultures, societies, groups, and individuals. Literary Studies examines the place of literature in society, and explores how we conceptualize and describe the world and ourselves.  

Literary Theories

There are a number of different theories about literature, why and how it is created. These theories influence how a work of literature is analyzed, interpreted, and understood. Literary Studies most often uses the method of textual analysis.

Linguistic Studies

Linguistics is the study of languages and their structures. Linguistic Studies examines how language is created and constructed, how it functions and is learned, and how we conceptualize and structure our world through our words.   

Language Theories

There are different theories about the creation and purpose of language. Some theories state that language is the result of the nature of society, while others emphasize the role of humans in constructing meaning. Linguistic Studies use methods such as textual analysis, ethnographic research, statistical modeling.

History Studies

History is the study of events, and their related ideas, individuals, and objects. History Studies examines how moments in time are connected, and how we make sense of things that happen.

Historiography  is the study of how historians have interpreted and written about historical events, in essence, how they perceive history itself. Traditionally, a historiography was a name for a history, literally a specific "writing of history".  

History Theories

There are many different theories about if and how events are related to one another, and these theories have influenced how history has been written about over the centuries. History Studies use methods such as textual analysis and archival research.

A related theory to history theories is Memory Theory , which considers how collective and individual memory is created and preserved. Memory Studies examines the ways in which events are recorded and remembered, or, alternatively, forgotten, and how we choose to create and remember (or forget) our past.

Anthropological Studies

Anthropology is the study of human societies, their behaviors and cultures. Anthropological Studies examine how societies are formed and function, and the many aspects which form our identities.

Social Anthropology  examines human behavior. Sometimes this sub-field is combined with Cultural Anthropology as Sociocultural Anthropology.

Cultural Anthropology  examines the cultures, or various beliefs and practices, of societies. Sometimes this sub-field is combined with Social Anthropology as Sociocultural Anthropology.

Physical Anthropology , also called Biological Anthropology, examines the biology of humans and how they interact with their environment.

Linguistic Anthropology  examines the place of language in shaping social life.

Archaeology  examines the material culture, or the objects, of humans. It is considered a sub-field of Anthropology in the United States, and a sub-field of History in other parts of the world.  

Ethnography is the study of a specific society using the methods of observation and immersion, or talking and living with individuals in order to understand them.   

Anthropological Theories

The is a long tradition of theories about how societies organize themselves and how they function. These theories determine how cultural beliefs and practices are understood, in essence, how we understand ourselves and others. Anthropology Studies use methods such as interviews, focus groups, observation, ethnographic research, and record keeping, as well as textual analysis and archival research.

Sociological Studies

Sociology is the study of societies, their behaviors, relationships, and interactions. It examines social order and social changes, trying to understand how and why we organize ourselves and relate to one another.

Historical Sociology   is the study of the behaviors and organization of societies of the past.   

Sociological Theories

There are different theories about how societies are structured and why they act the way they do. Sociological Studies often use the methods of surveys, experiments, ethnographic research, and textual analysis.

Sociological theories are theories about how the mechanics of societies function, whereas  Social Theory  encompasses more broadly theories which explain how societies think and act.

Geography Studies

Geography is the study of land, inhabitants, and natural phenomena. It examines the relationship between humans and their environment, and helps us to understand our relationship with the world. 

Human Geography  examines humans and their communities, and their relationships with place, space, and environment.

Physical Geography  examines the processes and patterns of environments, such as their atmosphere, hydrosphere, biosphere, and geosphere.

Cartography  is both the study of and the science and art of map-making. It reveals how we view and conceptualize the world and our relationship to it and to others.   

Geography Theories

There are a number of theories as to the relationship between humans and their environments, many of which are shared with the fields of Anthropology and Sociology. Geography Studies use a variety of research methods, including interviews, surveys, observation, and GIS or spatial analysis.

Cultural Studies is the study and analysis of culture. It is a cross-disciplinary field which examines the various aspects of a society, in order to understand how we form our identities. 

Culture  is the ideas, behaviors, customs, and objects of a region, society, group, or individual. 

Material culture   are the physical objects of a culture, such as tools, domestic objects, religious objects, works of art.  

Cultural Theories

Cultural theories draw upon theories in a variety of fields, including literary theories, semiotics, history theories, anthropological theories, social theories, museum studies, art history, and media studies. Cultural theories influence how we analyze and interpret the culture of societies. Cultural Studies tends to use methods such as interviews, observation, ethnographic research, record keeping, archival research, textual analysis, visual analysis.

Folklore Studies, also known as Folkloristics, is the study of the expressions of culture, particularly the practices and products of a society. Folklore Studies examines the things we make to understand how they make us.

Folklore  has been traditionally considered, narrowly, as the oral tales of a society. More broadly, the term refers to all aspects of a culture – beliefs, traditions, norms, behaviors, language, literature, jokes, music, art, foodways, tools, objects, etc.  

Folklore Theories

A number of theories have emerged over the years about how societies create themselves, and these theories influence how we view and understand the things which societies create. Folklore Studies use methods such as interviews, focus groups, observation, ethnographic research, and record keeping, as well as textual analysis, visual analysis and archival research.

Arts Studies

The arts are a range of disciplines which study, create, and engage with human expression. The arts include,

• Architecture -- Design
• Visual Arts -- Drawing, Painting, Illustration, Sculpting, Ceramics, Photography, Film
• Literary Arts -- Fiction, Drama, Poetry, Creative Writing, Storytelling
• Performance Arts -- Music, Dance, Theatre
• Textile Arts -- Fashion
• Craft -- Weaving, Woodwork, Paperwork, Glasswork, Jewelry-making
• Culinary Arts -- Cooking, Baking, Chocolate-making, Brewing, Wine-making
• Art History and Criticism

The arts are a collection of areas of studies which combine technical skills and creativity to produce objects which convey human experience.

Architecture  is the study and design of structures. It examines both the utilitarian and the sociological aspects of space, and the relationship between constructed space and humans. 

Art History  is the study and analysis of visual arts. 

Musicology  is the study and analysis of music.

Performance   is the study and the practice of art is time and space. 

Film & Media Studies  is the study of art which employs technologies.   

Art Theories

There are as many theories about the arts as there are areas of arts. These theories affect how we understand the identity and the agency of the artist, the meaning of the art, and the relationship between the art and society. Arts fields often employ textual and visual analysis research methods, as well as observation and experimentation. 

Folklorists study people's lives and thus they are responsible to preserve and protect culture. Folklorists are professionals and researchers and thus they have a responsibility to the field to uphold standards of behavior and work. Finally, folklorists interact with individuals and are responsible to uphold human rights. Though there is little direct legislation governing folklore studies, there are numerous laws concerning human rights and information, as well as professional standards in the field of cultural heritage preservation. 

Legislation

The codes of ethics and standards which govern folklore studies have been developed over time from a number of authorities.  

1948    United Nations, Universal Declaration of Human Rights

1948    American Anthropological Association, Resolution on Freedom of Publication

1971    American Anthropological Association, Principles of Professional Responsibility Statement of Ethics

1976    American Folklife Preservation Act (P.L. 94-201)

American Folklife Center established at the Library of Congress and given duty to preserve American folklife

1985    UNESCO, Protection of Expressions of Folklore Against Illicit Exploitation and Other Prejudicial Actions

1988    American Folklore Society, Statement of Ethics

1988    National Association for the Practice of Anthropology, Ethical Guidelines for Practitioners

1989    UNESCO, Recommendation on the Safeguarding of Traditional Culture and Folklore

1998    American Anthropological Association, Code of Ethics

2003    UNESCO, Convention for the Safeguarding of the Intangible Cultural Heritage

Handbook of Research Ethics and Scientific Integrity

Ron Iphofen, editor 2020

The Ethics of Research with Human Subjects

David B. Resnik 2018

The Ethics of Cultural Heritage

Tracy Ireland & John Schofeld 2014

Critical Ethnography

D. Soyini Madison 2005

Ethics in Ethnography

Margaret D. LeCompte & Jean J. Schensul 2015

The Ethics of Social Research

Joan E. Sieber, editor 1982

Research Ethics for Human Geography

Helen F. Wilson & Jonathan Darling, editors 2021

The Ethics of Cultural Studies

Joanna Zylinska 2005

Museum Collection Ethics

Steven Miller 2020

Theorizing Folklore from the Margins

Solimar Otero & Mintzi Auanda Martínez-Rivera, editors 2021

• << Previous: Background
• Next: Searching HOLLIS >>

Except where otherwise noted, this work is subject to a Creative Commons Attribution 4.0 International License , which allows anyone to share and adapt our material as long as proper attribution is given. For details and exceptions, see the Harvard Library Copyright Policy ©2021 Presidents and Fellows of Harvard College.

• Privacy Policy

Home » Narrative Analysis – Types, Methods and Examples

Narrative Analysis – Types, Methods and Examples

Table of Contents

Narrative Analysis

Definition:

Narrative analysis is a qualitative research methodology that involves examining and interpreting the stories or narratives people tell in order to gain insights into the meanings, experiences, and perspectives that underlie them. Narrative analysis can be applied to various forms of communication, including written texts, oral interviews, and visual media.

In narrative analysis, researchers typically examine the structure, content, and context of the narratives they are studying, paying close attention to the language, themes, and symbols used by the storytellers. They may also look for patterns or recurring motifs within the narratives, and consider the cultural and social contexts in which they are situated.

Types of Narrative Analysis

Types of Narrative Analysis are as follows:

Content Analysis

This type of narrative analysis involves examining the content of a narrative in order to identify themes, motifs, and other patterns. Researchers may use coding schemes to identify specific themes or categories within the text, and then analyze how they are related to each other and to the overall narrative. Content analysis can be used to study various forms of communication, including written texts, oral interviews, and visual media.

Structural Analysis

This type of narrative analysis focuses on the formal structure of a narrative, including its plot, character development, and use of literary devices. Researchers may analyze the narrative arc, the relationship between the protagonist and antagonist, or the use of symbolism and metaphor. Structural analysis can be useful for understanding how a narrative is constructed and how it affects the reader or audience.

Discourse Analysis

This type of narrative analysis focuses on the language and discourse used in a narrative, including the social and cultural context in which it is situated. Researchers may analyze the use of specific words or phrases, the tone and style of the narrative, or the ways in which social and cultural norms are reflected in the narrative. Discourse analysis can be useful for understanding how narratives are influenced by larger social and cultural structures.

Phenomenological Analysis

This type of narrative analysis focuses on the subjective experience of the narrator, and how they interpret and make sense of their experiences. Researchers may analyze the language used to describe experiences, the emotions expressed in the narrative, or the ways in which the narrator constructs meaning from their experiences. Phenomenological analysis can be useful for understanding how people make sense of their own lives and experiences.

Critical Analysis

This type of narrative analysis involves examining the political, social, and ideological implications of a narrative, and questioning its underlying assumptions and values. Researchers may analyze the ways in which a narrative reflects or reinforces dominant power structures, or how it challenges or subverts those structures. Critical analysis can be useful for understanding the role that narratives play in shaping social and cultural norms.

Autoethnography

This type of narrative analysis involves using personal narratives to explore cultural experiences and identity formation. Researchers may use their own personal narratives to explore issues such as race, gender, or sexuality, and to understand how larger social and cultural structures shape individual experiences. Autoethnography can be useful for understanding how individuals negotiate and navigate complex cultural identities.

Thematic Analysis

This method involves identifying themes or patterns that emerge from the data, and then interpreting these themes in relation to the research question. Researchers may use a deductive approach, where they start with a pre-existing theoretical framework, or an inductive approach, where themes are generated from the data itself.

Narrative Analysis Conducting Guide

Here are some steps for conducting narrative analysis:

• Identify the research question: Narrative analysis begins with identifying the research question or topic of interest. Researchers may want to explore a particular social or cultural phenomenon, or gain a deeper understanding of a particular individual’s experience.
• Collect the narratives: Researchers then collect the narratives or stories that they will analyze. This can involve collecting written texts, conducting interviews, or analyzing visual media.
• Transcribe and code the narratives: Once the narratives have been collected, they are transcribed into a written format, and then coded in order to identify themes, motifs, or other patterns. Researchers may use a coding scheme that has been developed specifically for the study, or they may use an existing coding scheme.
• Analyze the narratives: Researchers then analyze the narratives, focusing on the themes, motifs, and other patterns that have emerged from the coding process. They may also analyze the formal structure of the narratives, the language used, and the social and cultural context in which they are situated.
• Interpret the findings: Finally, researchers interpret the findings of the narrative analysis, and draw conclusions about the meanings, experiences, and perspectives that underlie the narratives. They may use the findings to develop theories, make recommendations, or inform further research.

Applications of Narrative Analysis

Narrative analysis is a versatile qualitative research method that has applications across a wide range of fields, including psychology, sociology, anthropology, literature, and history. Here are some examples of how narrative analysis can be used:

• Understanding individuals’ experiences: Narrative analysis can be used to gain a deeper understanding of individuals’ experiences, including their thoughts, feelings, and perspectives. For example, psychologists might use narrative analysis to explore the stories that individuals tell about their experiences with mental illness.
• Exploring cultural and social phenomena: Narrative analysis can also be used to explore cultural and social phenomena, such as gender, race, and identity. Sociologists might use narrative analysis to examine how individuals understand and experience their gender identity.
• Analyzing historical events: Narrative analysis can be used to analyze historical events, including those that have been recorded in literary texts or personal accounts. Historians might use narrative analysis to explore the stories of survivors of historical traumas, such as war or genocide.
• Examining media representations: Narrative analysis can be used to examine media representations of social and cultural phenomena, such as news stories, films, or television shows. Communication scholars might use narrative analysis to examine how news media represent different social groups.
• Developing interventions: Narrative analysis can be used to develop interventions to address social and cultural problems. For example, social workers might use narrative analysis to understand the experiences of individuals who have experienced domestic violence, and then use that knowledge to develop more effective interventions.

Examples of Narrative Analysis

Here are some examples of how narrative analysis has been used in research:

• Personal narratives of illness: Researchers have used narrative analysis to examine the personal narratives of individuals living with chronic illness, to understand how they make sense of their experiences and construct their identities.
• Oral histories: Historians have used narrative analysis to analyze oral histories to gain insights into individuals’ experiences of historical events and social movements.
• Children’s stories: Researchers have used narrative analysis to analyze children’s stories to understand how they understand and make sense of the world around them.
• Personal diaries : Researchers have used narrative analysis to examine personal diaries to gain insights into individuals’ experiences of significant life events, such as the loss of a loved one or the transition to adulthood.
• Memoirs : Researchers have used narrative analysis to analyze memoirs to understand how individuals construct their life stories and make sense of their experiences.
• Life histories : Researchers have used narrative analysis to examine life histories to gain insights into individuals’ experiences of migration, displacement, or social exclusion.

Purpose of Narrative Analysis

The purpose of narrative analysis is to gain a deeper understanding of the stories that individuals tell about their experiences, identities, and beliefs. By analyzing the structure, content, and context of these stories, researchers can uncover patterns and themes that shed light on the ways in which individuals make sense of their lives and the world around them.

The primary purpose of narrative analysis is to explore the meanings that individuals attach to their experiences. This involves examining the different elements of a story, such as the plot, characters, setting, and themes, to identify the underlying values, beliefs, and attitudes that shape the story. By analyzing these elements, researchers can gain insights into the ways in which individuals construct their identities, understand their relationships with others, and make sense of the world.

Narrative analysis can also be used to identify patterns and themes across multiple stories. This involves comparing and contrasting the stories of different individuals or groups to identify commonalities and differences. By analyzing these patterns and themes, researchers can gain insights into broader cultural and social phenomena, such as gender, race, and identity.

In addition, narrative analysis can be used to develop interventions that address social and cultural problems. By understanding the stories that individuals tell about their experiences, researchers can develop interventions that are tailored to the unique needs of different individuals and groups.

Overall, the purpose of narrative analysis is to provide a rich, nuanced understanding of the ways in which individuals construct meaning and make sense of their lives. By analyzing the stories that individuals tell, researchers can gain insights into the complex and multifaceted nature of human experience.

When to use Narrative Analysis

Here are some situations where narrative analysis may be appropriate:

• Studying life stories: Narrative analysis can be useful in understanding how individuals construct their life stories, including the events, characters, and themes that are important to them.
• Analyzing cultural narratives: Narrative analysis can be used to analyze cultural narratives, such as myths, legends, and folktales, to understand their meanings and functions.
• Exploring organizational narratives: Narrative analysis can be helpful in examining the stories that organizations tell about themselves, their histories, and their values, to understand how they shape the culture and practices of the organization.
• Investigating media narratives: Narrative analysis can be used to analyze media narratives, such as news stories, films, and TV shows, to understand how they construct meaning and influence public perceptions.
• Examining policy narratives: Narrative analysis can be helpful in examining policy narratives, such as political speeches and policy documents, to understand how they construct ideas and justify policy decisions.

Characteristics of Narrative Analysis

Here are some key characteristics of narrative analysis:

• Focus on stories and narratives: Narrative analysis is concerned with analyzing the stories and narratives that people tell, whether they are oral or written, to understand how they shape and reflect individuals’ experiences and identities.
• Emphasis on context: Narrative analysis seeks to understand the context in which the narratives are produced and the social and cultural factors that shape them.
• Interpretive approach: Narrative analysis is an interpretive approach that seeks to identify patterns and themes in the stories and narratives and to understand the meaning that individuals and communities attach to them.
• Iterative process: Narrative analysis involves an iterative process of analysis, in which the researcher continually refines their understanding of the narratives as they examine more data.
• Attention to language and form : Narrative analysis pays close attention to the language and form of the narratives, including the use of metaphor, imagery, and narrative structure, to understand the meaning that individuals and communities attach to them.
• Reflexivity : Narrative analysis requires the researcher to reflect on their own assumptions and biases and to consider how their own positionality may shape their interpretation of the narratives.
• Qualitative approach: Narrative analysis is typically a qualitative research method that involves in-depth analysis of a small number of cases rather than large-scale quantitative studies.

Advantages of Narrative Analysis

Here are some advantages of narrative analysis:

• Rich and detailed data : Narrative analysis provides rich and detailed data that allows for a deep understanding of individuals’ experiences, emotions, and identities.
• Humanizing approach: Narrative analysis allows individuals to tell their own stories and express their own perspectives, which can help to humanize research and give voice to marginalized communities.
• Holistic understanding: Narrative analysis allows researchers to understand individuals’ experiences in their entirety, including the social, cultural, and historical contexts in which they occur.
• Flexibility : Narrative analysis is a flexible research method that can be applied to a wide range of contexts and research questions.
• Interpretive insights: Narrative analysis provides interpretive insights into the meanings that individuals attach to their experiences and the ways in which they construct their identities.
• Appropriate for sensitive topics: Narrative analysis can be particularly useful in researching sensitive topics, such as trauma or mental health, as it allows individuals to express their experiences in their own words and on their own terms.
• Can lead to policy implications: Narrative analysis can provide insights that can inform policy decisions and interventions, particularly in areas such as health, education, and social policy.

Limitations of Narrative Analysis

Here are some of the limitations of narrative analysis:

• Subjectivity : Narrative analysis relies on the interpretation of researchers, which can be influenced by their own biases and assumptions.
• Limited generalizability: Narrative analysis typically involves in-depth analysis of a small number of cases, which limits its generalizability to broader populations.
• Ethical considerations: The process of eliciting and analyzing narratives can raise ethical concerns, particularly when sensitive topics such as trauma or abuse are involved.
• Limited control over data collection: Narrative analysis often relies on data that is already available, such as interviews, oral histories, or written texts, which can limit the control that researchers have over the quality and completeness of the data.
• Time-consuming: Narrative analysis can be a time-consuming research method, particularly when analyzing large amounts of data.
• Interpretation challenges: Narrative analysis requires researchers to make complex interpretations of data, which can be challenging and time-consuming.
• Limited statistical analysis: Narrative analysis is typically a qualitative research method that does not lend itself well to statistical analysis.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Cluster Analysis – Types, Methods and Examples

Grounded Theory – Methods, Examples and Guide

Documentary Analysis – Methods, Applications and...

Phenomenology – Methods, Examples and Guide

Methodological Framework – Types, Examples and...

Discriminant Analysis – Methods, Types and...

A Brief History of Qualitative Research

• First Online: 29 September 2022

Cite this chapter

• Robert E. White   ORCID: orcid.org/0000-0002-8045-164X 3 &
• Karyn Cooper 4  

1660 Accesses

As so often happens with matters of research, it is generally thought that quantitative research is the father of modern qualitative research. At face value, this may be true, but the reality is much more convoluted. In order to gain a perspective on the beginnings of qualitative research, we must return to Ancient Greece.

The question of understanding the other and understanding oneself by understanding the other, that is the goal of what is now called qualitative research. Stephen Kemmis, Charles Sturt University

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Get 10 units per month
• Download Article/Chapter or Ebook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Anagnostopoulos, G. (2013). Aristotle’s life. In G. Anagnostopoulos (Ed.), A companion to Aristotle (pp. 1–13). Wiley-Blackwell.

Google Scholar  

Aveling, E.-L., Gillespie, A., & Cornish, F. (2015). A qualitative method for analyzing multivoicedness. Qualitative Research, 15 (6), 670–687.

Article   Google Scholar  

Barnes, J. (1995). Life and work. In J. Barnes (Ed.), The Cambridge companion to Aristotle (pp. 12–26). Cambridge University Press.

Beilharz, P. (2000). Zygmunt Bauman: The dialectic of modernity . Sage.

Book   Google Scholar  

Brinkmann, S., Jacobsen, M. H., & Kristiansen, S. (2014). Historical overview of qualitative research in the social sciences. In P. Leavy (Ed.), The Oxford handbook of qualitative research (pp. 17–42). Oxford University Press.

Brumbaugh, R. S. (1991). Plato for the modern age . University Press of America.

Camus, A. (1955). The myth of Sisyphus (Trans. J. O’Brien). Alfred A. Knopf.

Carroll, L. (1865/2018). Alice’s adventures in wonderland . Wisehouse Classics.

Clifford, J. (1988). The predicament of culture: Twentieth-century ethnography, literature, and art . Harvard University Press.

Clifford, J. (1997). Routes: Travel and translation in the late twentieth century. Harvard University Press.

Cooper, J. M., & Hutchinson, D. S. (1997). Introduction. In J. M. Cooper & D. S. Hutchinson (Eds.), Plato: Complete works (pp. vii–xxvi). Hackett Publishing.

Cooper, K., & White, R. E. (2012). Qualitative research in the postmodern era: Contexts of qualitative research . Springer.

Crichton, M. (2009). State of fear . Harper Collins.

Denzin, N. K. (2008). Evolution of qualitative research. In L. M. Given (Ed.), The SAGE encyclopedia of qualitative research methods (pp. 311–318). Sage.

Denzin, N. K., & Lincoln, Y. S. (2005). Introduction: The discipline and practice of qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (3rd ed., pp. 1–32). Sage.

Denzin, N. K., & Lincoln, Y. S. (2008). Introduction: The discipline and practice of qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), Collecting and interpreting qualitative materials (3rd ed., pp. 1–44). Sage.

Derrida, J. (1976). Of grammatology (Trans. G. C. Spivak). Johns Hopkins University Press.

Durant, W. (2006). The story of philosophy . Simon & Schuster.

Geertz, C. (1973). The interpretation of cultures: Selected essays . Basic Books.

Geertz, C. (1983). Local knowledge: Further essays in interpretive anthropology . Basic Books.

Gilbert, H., & Tompkins, J. (1996). Post-colonial drama: Theory, practice, politics . Routledge.

Grodecki, L. (1991). Gothic architecture . Rizzoli.

Jori, A. (2003). Aristotele . Bruno Mondadori Editore.

Kahn, C. H. (1998). Plato and the Socratic dialogue: The philosophical use of a literary form . Cambridge University Press.

Kofman, S. (1998). Socrates: Fictions of a philosopher . Cornell University Press.

Kraut, R. (2013). Plato. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy . Stanford University Press.

Magee, B. (2016). The story of philosophy. A concise introduction to the world’s greatest thinkers and their ideas . Dorling Kindersley Books.

Mead, M. (1928). Coming of age in Samoa . Williams Morrow & Company.

Neill, A., & Ridley, A. (1995). Philosophy of art: Readings ancient and modern . McGraw-Hill.

Orans, M. (1996). Not even wrong: Margaret Mead, Derek Freeman and the Samoans . Chandler & Sharp.

Pastoureau, M. (2001). Blue: The history of a color . Princeton University Press.

Rivet, F. (2014). In the footsteps of Abraham Ulrikab: The events of 1880–1881 . Polar Horizons.

Rosaldo, R. (1989). Culture and truth: The remaking of social analysis . Beacon Press.

Russell, B. (1972). A history of western philosophy . Simon & Schuster.

Sartre, J.-P. (1946). Existentialism is a humanism . Methuen.

Seale, C. (2004). History of qualitative methods. In C. Seale (Ed.), Researching society and culture (2nd ed., pp. 99–113). Sage.

Sharp, J. (2008). Geographies of postcolonialism . Sage.

Tomkiss, F. (2004). History of social statistics and the social survey. In C. Seale (Ed.), Researching society and culture (2nd ed., pp. 85–99). Sage.

Vidich, A. J., & Lyman, S. M. (2000). Qualitative methods: Their history in sociology and anthropology. In N. K. Denzin & Y. S. Lincoln (Eds.), The handbook of qualitative research (2nd ed., pp. 37–84). Sage.

Wenning, C. J. (2009). Scientific epistemology: How scientists know what they know. Journal of Physics Teacher Education, 5 (2), 1–15. Retrieved October 21, 2019, from http://www2.phy.ilstu.edu/pte/publications/scientific_epistemology.pdf

Wertz, F. J. (2014). Qualitative inquiry in the history of psychology. Qualitative Psychology, 1 (1), 4–16.

Download references

Author information

Authors and affiliations.

Faculty of Education, St. Francis Xavier University, Antigonish, NS, Canada

Robert E. White

OISE, University of Toronto, Toronto, ON, Canada

Karyn Cooper

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Robert E. White .

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

White, R.E., Cooper, K. (2022). A Brief History of Qualitative Research. In: Qualitative Research in the Post-Modern Era. Springer, Cham. https://doi.org/10.1007/978-3-030-85124-8_1

Download citation

DOI : https://doi.org/10.1007/978-3-030-85124-8_1

Published : 29 September 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-85126-2

Online ISBN : 978-3-030-85124-8

eBook Packages : Education Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

5 Types of Qualitative Methods

But just as with quantitative methods, there are actually many varieties of qualitative methods.

Similar to the way you can group usability testing methods , there are also a number of ways to segment qualitative methods.

A popular and helpful categorization separate qualitative methods into five groups: ethnography, narrative, phenomenological, grounded theory, and case study. John Creswell outlines these five methods in Qualitative Inquiry and Research Design .

While the five methods generally use similar data collection techniques (observation, interviews, and reviewing text), the purpose of the study differentiates them—something similar with different types of usability tests . And like classifying different usability studies, the differences between the methods can be a bit blurry. Here are the five qualitative methods in more detail.

1. Ethnography

Ethnographic research is probably the most familiar and applicable type of qualitative method to UX professionals. In ethnography, you immerse yourself in the target participants’ environment to understand the goals, cultures, challenges, motivations, and themes that emerge. Ethnography has its roots in cultural anthropology where researchers immerse themselves within a culture, often for years! Rather than relying on interviews or surveys, you experience the environment first hand, and sometimes as a “participant observer.”

For example, one way of uncovering the unmet needs of customers is to “ follow them home ” and observe them as they interact with the product. You don’t come armed with any hypotheses to necessarily test; rather, you’re looking to find out how a product is used.

2. Narrative

The narrative approach weaves together a sequence of events, usually from just one or two individuals to form a cohesive story. You conduct in-depth interviews, read documents, and look for themes; in other words, how does an individual story illustrate the larger life influences that created it. Often interviews are conducted over weeks, months, or even years, but the final narrative doesn’t need to be in chronological order. Rather it can be presented as a story (or narrative) with themes, and can reconcile conflicting stories and highlight tensions and challenges which can be opportunities for innovation.

For example, a narrative approach can be an appropriate method for building a persona . While a persona should be built using a mix of methods—including segmentation analysis from surveys—in-depth interviews with individuals in an identified persona can provide the details that help describe the culture, whether it’s a person living with Multiple Sclerosis, a prospective student applying for college, or a working mom.

3. Phenomenological

When you want to describe an event, activity, or phenomenon, the aptly named phenomenological study is an appropriate qualitative method. In a phenomenological study, you use a combination of methods, such as conducting interviews, reading documents, watching videos, or visiting places and events, to understand the meaning participants place on whatever’s being examined. You rely on the participants’ own perspectives to provide insight into their motivations.

Like other qualitative methods, you don’t start with a well-formed hypothesis. In a phenomenological study, you often conduct a lot of interviews, usually between 5 and 25 for common themes , to build a sufficient dataset to look for emerging themes and to use other participants to validate your findings.

For example, there’s been an explosion in the last 5 years in online courses and training. But how do students engage with these courses? While you can examine time spent and content accessed using log data and even assess student achievement vis-a-vis in-person courses, a phenomenological study would aim to better understand the students experience and how that may impact comprehension of the material.

4. Grounded Theory

Whereas a phenomenological study looks to describe the essence of an activity or event, grounded theory looks to provide an explanation or theory behind the events. You use primarily interviews and existing documents to build a theory based on the data. You go through a series of open and axial coding techniques to identify themes and build the theory. Sample sizes are often also larger—between 20 to 60—with these studies to better establish a theory. Grounded theory can help inform design decisions by better understanding how a community of users currently use a product or perform tasks.

For example, a grounded theory study could involve understanding how software developers use portals to communicate and write code or how small retail merchants approve or decline customers for credit.

5. Case Study

Made famous by the Harvard Business School, even mainly quantitative researchers can relate to the value of the case study in explaining an organization, entity, company, or event. A case study involves a deep understanding through multiple types of data sources. Case studies can be explanatory, exploratory, or describing an event. The annual CHI conference has a peer-reviewed track dedicated to case studies.

For example, a case study of how a large multi-national company introduced UX methods into an agile development environment would be informative to many organizations.

The table below summarizes the differences between the five qualitative methods.

You might also be interested in

Data Analysis in Research

Ai generator.

Data analysis in research involves systematically applying statistical and logical techniques to describe, illustrate, condense, and evaluate data. It is a crucial step that enables researchers to identify patterns, relationships, and trends within the data, transforming raw information into valuable insights. Through methods such as descriptive statistics, inferential statistics, and qualitative analysis, researchers can interpret their findings, draw conclusions, and support decision-making processes. An effective data analysis plan and robust methodology ensure the accuracy and reliability of research outcomes, ultimately contributing to the advancement of knowledge across various fields.

What is Data Analysis in Research?

Data analysis in research involves using statistical and logical techniques to describe, summarize, and compare collected data. This includes inspecting, cleaning, transforming, and modeling data to find useful information and support decision-making. Quantitative data provides measurable insights, and a solid research design ensures accuracy and reliability. This process helps validate hypotheses, identify patterns, and make informed conclusions, making it a crucial step in the scientific method.

Examples of Data analysis in Research

• Survey Analysis : Researchers collect survey responses from a sample population to gauge opinions, behaviors, or characteristics. Using descriptive statistics, they summarize the data through means, medians, and modes, and then inferential statistics to generalize findings to a larger population.
• Experimental Analysis : In scientific experiments, researchers manipulate one or more variables to observe the effect on a dependent variable. Data is analyzed using methods such as ANOVA or regression analysis to determine if changes in the independent variable(s) significantly affect the dependent variable.
• Content Analysis : Qualitative research often involves analyzing textual data, such as interview transcripts or open-ended survey responses. Researchers code the data to identify recurring themes, patterns, and categories, providing a deeper understanding of the subject matter.
• Correlation Studies : Researchers explore the relationship between two or more variables using correlation coefficients. For example, a study might examine the correlation between hours of study and academic performance to identify if there is a significant positive relationship.
• Longitudinal Analysis : This type of analysis involves collecting data from the same subjects over a period of time. Researchers analyze this data to observe changes and developments, such as studying the long-term effects of a specific educational intervention on student achievement.
• Meta-Analysis : By combining data from multiple studies, researchers perform a meta-analysis to increase the overall sample size and enhance the reliability of findings. This method helps in synthesizing research results to draw broader conclusions about a particular topic or intervention.

Data analysis in Qualitative Research

Data analysis in qualitative research involves systematically examining non-numeric data, such as interviews, observations, and textual materials, to identify patterns, themes, and meanings. Here are some key steps and methods used in qualitative data analysis:

• Coding : Researchers categorize the data by assigning labels or codes to specific segments of the text. These codes represent themes or concepts relevant to the research question.
• Thematic Analysis : This method involves identifying and analyzing patterns or themes within the data. Researchers review coded data to find recurring topics and construct a coherent narrative around these themes.
• Content Analysis : A systematic approach to categorize verbal or behavioral data to classify, summarize, and tabulate the data. This method often involves counting the frequency of specific words or phrases.
• Narrative Analysis : Researchers focus on the stories and experiences shared by participants, analyzing the structure, content, and context of the narratives to understand how individuals make sense of their experiences.
• Grounded Theory : This method involves generating a theory based on the data collected. Researchers collect and analyze data simultaneously, continually refining and adjusting their theoretical framework as new data emerges.
• Discourse Analysis : Examining language use and communication patterns within the data, researchers analyze how language constructs social realities and power relationships.
• Case Study Analysis : An in-depth analysis of a single case or multiple cases, exploring the complexities and unique aspects of each case to gain a deeper understanding of the phenomenon under study.

Data analysis in Quantitative Research

Data analysis in quantitative research involves the systematic application of statistical techniques to numerical data to identify patterns, relationships, and trends. Here are some common methods used in quantitative data analysis:

• Descriptive Statistics : This includes measures such as mean, median, mode, standard deviation, and range, which summarize and describe the main features of a data set.
• Inferential Statistics : Techniques like t-tests, chi-square tests, and ANOVA (Analysis of Variance) are used to make inferences or generalizations about a population based on a sample.
• Regression Analysis : This method examines the relationship between dependent and independent variables. Simple linear regression analyzes the relationship between two variables, while multiple regression examines the relationship between one dependent variable and several independent variables.
• Correlation Analysis : Researchers use correlation coefficients to measure the strength and direction of the relationship between two variables.
• Factor Analysis : This technique is used to identify underlying relationships between variables by grouping them into factors based on their correlations.
• Cluster Analysis : A method used to group a set of objects or cases into clusters, where objects in the same cluster are more similar to each other than to those in other clusters.
• Hypothesis Testing : This involves testing an assumption or hypothesis about a population parameter. Common tests include z-tests, t-tests, and chi-square tests, which help determine if there is enough evidence to reject the null hypothesis.
• Time Series Analysis : This method analyzes data points collected or recorded at specific time intervals to identify trends, cycles, and seasonal variations.
• Multivariate Analysis : Techniques like MANOVA (Multivariate Analysis of Variance) and PCA (Principal Component Analysis) are used to analyze data that involves multiple variables to understand their effect and relationships.
• Structural Equation Modeling (SEM) : A multivariate statistical analysis technique that is used to analyze structural relationships. This method is a combination of factor analysis and multiple regression analysis and is used to analyze the structural relationship between measured variables and latent constructs.

Data analysis in Research Methodology

Data analysis in research methodology involves the process of systematically applying statistical and logical techniques to describe, condense, recap, and evaluate data. Here are the key components and methods involved:

• Data Preparation : This step includes collecting, cleaning, and organizing raw data. Researchers ensure data quality by handling missing values, removing duplicates, and correcting errors.
• Descriptive Analysis : Researchers use descriptive statistics to summarize the basic features of the data. This includes measures such as mean, median, mode, standard deviation, and graphical representations like histograms and pie charts.
• Inferential Analysis : This involves using statistical tests to make inferences about the population from which the sample was drawn. Common techniques include t-tests, chi-square tests, ANOVA, and regression analysis.
• Qualitative Data Analysis : For non-numeric data, researchers employ methods like coding, thematic analysis, content analysis, narrative analysis, and discourse analysis to identify patterns and themes.
• Quantitative Data Analysis : For numeric data, researchers apply statistical methods such as correlation, regression, factor analysis, cluster analysis, and time series analysis to identify relationships and trends.
• Hypothesis Testing : Researchers test hypotheses using statistical methods to determine whether there is enough evidence to reject the null hypothesis. This involves calculating p-values and confidence intervals.
• Data Interpretation : This step involves interpreting the results of the data analysis. Researchers draw conclusions based on the statistical findings and relate them back to the research questions and objectives.
• Validation and Reliability : Ensuring the validity and reliability of the analysis is crucial. Researchers check for consistency in the results and use methods like cross-validation and reliability testing to confirm their findings.
• Visualization : Effective data visualization techniques, such as charts, graphs, and plots, are used to present the data in a clear and understandable manner, aiding in the interpretation and communication of results.
• Reporting : The final step involves reporting the results in a structured format, often including an introduction, methodology, results, discussion, and conclusion. This report should clearly convey the findings and their implications for the research question.

Types of Data analysis in Research

• Purpose : To summarize and describe the main features of a dataset.
• Methods : Mean, median, mode, standard deviation, frequency distributions, and graphical representations like histograms and pie charts.
• Example : Calculating the average test scores of students in a class.
• Purpose : To make inferences or generalizations about a population based on a sample.
• Methods : T-tests, chi-square tests, ANOVA (Analysis of Variance), regression analysis, and confidence intervals.
• Example : Testing whether a new teaching method significantly affects student performance compared to a traditional method.
• Purpose : To analyze data sets to find patterns, anomalies, and test hypotheses.
• Methods : Visualization techniques like box plots, scatter plots, and heat maps; summary statistics.
• Example : Visualizing the relationship between hours of study and exam scores using a scatter plot.
• Purpose : To make predictions about future outcomes based on historical data.
• Methods : Regression analysis, machine learning algorithms (e.g., decision trees, neural networks), and time series analysis.
• Example : Predicting student graduation rates based on their academic performance and demographic data.
• Purpose : To provide recommendations for decision-making based on data analysis.
• Methods : Optimization algorithms, simulation, and decision analysis.
• Example : Suggesting the best course of action for improving student retention rates based on various predictive factors.
• Purpose : To identify and understand cause-and-effect relationships.
• Methods : Controlled experiments, regression analysis, path analysis, and structural equation modeling (SEM).
• Example : Determining the impact of a specific intervention, like a new curriculum, on student learning outcomes.
• Purpose : To understand the specific mechanisms through which variables affect one another.
• Methods : Detailed modeling and simulation, often used in scientific research to understand biological or physical processes.
• Example : Studying how a specific drug interacts with biological pathways to affect patient health.

How to write Data analysis in Research

Data analysis is crucial for interpreting collected data and drawing meaningful conclusions. Follow these steps to write an effective data analysis section in your research.

1. Prepare Your Data

Ensure your data is clean and organized:

• Remove duplicates and irrelevant data.
• Check for errors and correct them.
• Categorize data if necessary.

2. Choose the Right Analysis Method

Select a method that fits your data type and research question:

• Quantitative Data : Use statistical analysis such as t-tests, ANOVA, regression analysis.
• Qualitative Data : Use thematic analysis, content analysis, or narrative analysis.

3. Describe Your Analytical Techniques

Clearly explain the methods you used:

• Software and Tools : Mention any software (e.g., SPSS, NVivo) used.
• Statistical Tests : Detail the statistical tests applied, such as chi-square tests or correlation analysis.
• Qualitative Techniques : Describe coding and theme identification processes.

4. Present Your Findings

Organize your findings logically:

• Use Tables and Figures : Display data in tables, graphs, and charts for clarity.
• Summarize Key Results : Highlight the most significant findings.
• Include Relevant Statistics : Report p-values, confidence intervals, means, and standard deviations.

5. Interpret the Results

Explain what your findings mean in the context of your research:

• Compare with Hypotheses : State whether the results support your hypotheses.
• Relate to Literature : Compare your results with previous studies.
• Discuss Implications : Explain the significance of your findings.

6. Discuss Limitations

Acknowledge any limitations in your data or analysis:

• Sample Size : Note if the sample size was small.
• Biases : Mention any potential biases in data collection.
• External Factors : Discuss any factors that might have influenced the results.

7. Conclude with a Summary

Wrap up your data analysis section:

• Restate Key Findings : Briefly summarize the main results.
• Future Research : Suggest areas for further investigation.

Importance of Data analysis in Research

Data analysis is a fundamental component of the research process. Here are five key points highlighting its importance:

• Enhances Accuracy and Reliability Data analysis ensures that research findings are accurate and reliable. By using statistical techniques, researchers can minimize errors and biases, ensuring that the results are dependable.
• Facilitates Informed Decision-Making Through data analysis, researchers can make informed decisions based on empirical evidence. This is crucial in fields like healthcare, business, and social sciences, where decisions impact policies, strategies, and outcomes.
• Identifies Trends and Patterns Analyzing data helps researchers uncover trends and patterns that might not be immediately visible. These insights can lead to new hypotheses and areas of study, advancing knowledge in the field.
• Supports Hypothesis Testing Data analysis is vital for testing hypotheses. Researchers can use statistical methods to determine whether their hypotheses are supported or refuted, which is essential for validating theories and advancing scientific understanding.
• Provides a Basis for Predictions By analyzing current and historical data, researchers can develop models that predict future outcomes. This predictive capability is valuable in numerous fields, including economics, climate science, and public health.

FAQ’s

What is the difference between qualitative and quantitative data analysis.

Qualitative analysis focuses on non-numerical data to understand concepts, while quantitative analysis deals with numerical data to identify patterns and relationships.

What is descriptive statistics?

Descriptive statistics summarize and describe the features of a data set, including measures like mean, median, mode, and standard deviation.

What is inferential statistics?

Inferential statistics use sample data to make generalizations about a larger population, often through hypothesis testing and confidence intervals.

What is regression analysis?

Regression analysis examines the relationship between dependent and independent variables, helping to predict outcomes and understand variable impacts.

What is the role of software in data analysis?

Software like SPSS, R, and Excel facilitate data analysis by providing tools for statistical calculations, visualization, and data management.

What are data visualization techniques?

Data visualization techniques include charts, graphs, and maps, which help in presenting data insights clearly and effectively.

What is data cleaning?

Data cleaning involves removing errors, inconsistencies, and missing values from a data set to ensure accuracy and reliability in analysis.

What is the significance of sample size in data analysis?

Sample size affects the accuracy and generalizability of results; larger samples generally provide more reliable insights.

How does correlation differ from causation?

Correlation indicates a relationship between variables, while causation implies one variable directly affects the other.

What are the ethical considerations in data analysis?

Ethical considerations include ensuring data privacy, obtaining informed consent, and avoiding data manipulation or misrepresentation.

Text prompt

• Instructive
• Professional

10 Examples of Public speaking

20 Examples of Gas lighting

• Open access
• Published: 27 June 2024

Compassionate Othering: the construction of refugee patients in medical students’ narratives – a qualitative study using story completion

• Lena Bauer 1 ,
• Andreas Wienke 1 &
• Amand Führer 1  

BMC Medical Education volume  24 , Article number:  703 ( 2024 ) Cite this article

Metrics details

Refugees remain a marginalized population and are exposed to a variety of discriminatory processes, among them Othering which categorizes people as belonging or not-belonging according to certain ascribed characteristics. We explored how the narrative construction of refugee patients by medical students constitutes a form of Othering.

Using story completion , 124 5th year medical students at the Martin- Luther- University Halle-Wittenberg in October 2019 wrote a fictional story in response to a story stem situated in a medical practice. In a comparative approach, one patient presenting with abdominal pain lacks further characterization (version A) and the other is a refugee (version B). The stories were coded using qualitative content analysis by Mayring with a focus on content and narrative strategies (plot structure and perspective).

We identified four themes: characters, medical condition, access to care and provision of substandard care. The stories were predominantly framed with a medical or an interaction-based plot structure and written from a process-oriented perspective. The themes in version B, supported by their use of narrative strategies, were largely contextualized within the patients’ history of migration. An empathic depiction of patient B and the students’ compassion for the patients facing substandard care were key motifs as well.

The perception of the version B patients predominantly as refugees establishes their construction as an Other. The students’ compassion acts as a representation of societal inequalities and remains an inept response without the tools to counter underlying discriminatory structures. Based on a discourse of deservingness, compassion alone therefore perpetuates Othering and highlights the need for structural competency training in medical school.

Peer Review reports

In the last decade, Germany has witnessed a considerable increase in immigration by refugee populations comprised of a wide spectrum of nationalities, ethnicities, and age groups [ 1 ]. This development poses a variety of challenges, including numerous for the German health care system. Various structural barriers restrict access to care for patients with a migration background [ 2 , 3 ]. Furthermore, difficulties in the interaction between patients and medical personnel can also present obstacles to adequate care [ 4 , 5 ]: Salient factors include an oftentimes inadequate handling of language discrepancies [ 6 , 7 ], low levels of intercultural competence [ 8 , 9 ] or medical personnel’s hostile attitude [ 5 , 9 , 10 , 11 ]. This often results in the exclusion of refugee patients from health care services, in misdiagnoses, and in mistreatments.

From a social science point of view, many of the problems migrants face in the health care system have been linked to Othering [ 12 , 13 , 14 , 15 ]. Hereby, refugees are subjected to processes that mark them as different from other patients which in turn serves as a justification for their discrimination.

The roots of Othering theory are manifold. The following intends to showcase some of the most relevant strands of theorizing for our topic. We hereby mostly situate ourselves in feminist and postcolonial theory for their usefulness to grasp power dynamics and hierarchies. We do not touch upon other concepts that address alterity or difference [ 16 , 17 , 18 ] that are not central to our analysis.

In feminist theory, the concept of Othering dates back to Simone de Beauvoir’s pivotal work The second sex [ 19 ]. Beauvoir argues that in dominant societal perception man represents the social norm and woman is considered a deviation from said norm. The identity of the woman is therefore constructed in reference to man – more precisely the lack thereof being the defining characteristic. “She […] is the inessential in front of the essential. He is the Subject; he is the Absolute. She is the Other” [ 19 ]. Beauvoir not only criticizes the power of definition based in alterity, but she also denounces the resulting legal and economic disadvantages for women.

Another strand of theorizing comes from postcolonial scholarship: In reference to orientalist discourse, Edward Said asserts that “the Orient” has been constructed as the Other in relation to the Subject, “the Occident”, as he is describing the exoticizing and pathologizing portrayal of the Orient prevalent in Europe during the post-Enlightenment period [ 20 ]. Said describes a discourse that constructs “the Orient” from a European point of view, in academic contexts and as an imaginative idea in literature. He hereby shows that the contextualized discourse of Orientalism acts as a way of exercising social and political power over peoples perceived to constitute “the Orient”.

Postcolonial feminist scholar Gayati Chakravorty Spivak incorporated these lines of reasoning when she examined the role of marginalized women in postcolonial sites considering current global power dynamics [ 21 , 22 , 23 , 24 ]. She describes their perpetual marginalization and criticizes the lack of voice of Subaltern women, the Other, in the global feminist discourse. Similar dynamics can be found “along any social dimension” [ 25 ]. In recent years, this approach has therefore been taken up and used to describe the situation of various subaltern groups.

In the field of migration studies, an extensive body of literature describes Othering processes pertaining to refugees [ 12 , 14 , 25 , 26 , 27 , 28 , 29 ]. This line of research is inherently shaped by feminist and postcolonial scholarship and explores the ways in which immigrant groups are designated as subordinate and not belonging. This is oftentimes depicted and criticized as a process of categorization, creating categories of “us” and “them”, such as nationals and refugees [ 30 , 31 ]. The oversimplifying dichotomy of these categories constructs a social identity and presumed belonging to a social group based on a single characteristic: a person’s residence status. Refugees, as a specific subgroup of immigrants in general, are frequently categorized as one vulnerable Other lacking in agency and individuality and – if proven to be truly in need – deserving help.

Nevertheless, the image of “the refugee” as a national threat persists as well. Olsen et al. argue that the construction of “the refugee” as helpless and vulnerable enables the positioning of oneself as the dominant group and thus serves to ensure the maintenance of existent global power dynamics and national identity [ 32 ].

In health care literature, the mechanisms of Othering have been examined, for instance, with regard to public health crises such as the HIV/AIDS epidemic [ 13 , 33 , 34 , 35 , 36 ] and more recently the Covid-19 pandemic [ 37 , 38 ]. Prevalent themes hereby include the stigmatization of minority groups, the precarity of health care provision as well as differing responses to public health recommendations, creating a mentality of “us-vs-them” [ 39 ]. Similar themes have been described in psychiatric and mental health literature [ 40 , 41 , 42 , 43 , 44 ] as well as nursing literature [ 45 ].

Another facet of Othering has been highlighted by medical anthropologists studying humanitarian interventions and its moral legitimization as acts of compassion trying to alleviate suffering [ 46 , 47 ]. The focus on the suffering body has been criticized as it leads to a practice of favoring certain groups and injuries over others depending on their perceived deservingness [ 48 ]. Authors have also pointed out the inherent moral hierarchy between humanitarian actors and those they intend to help [ 49 , 50 ].

In summary, Othering exemplifies a mechanism that is ubiquitous in the construction of people and the ideas by which they are represented. Individuals are thereby categorized as belonging or not-belonging according to distinguishing characteristics. In the case of refugee patients, the failure to address these processes is especially momentous in a context as sensitive as health and leads to deficiencies in medical practice [ 51 , 52 ].

As prospective medical professionals, students hold a pivotal role in the provision of health care and on the future trajectory of the discourses pertaining to refugee patients. Still, as of now we know very little about medical students’ discursive approach towards patients with a history of refugee migration. Employing the technique of story completion , this study therefore intends to explore the construction of refugee patients by German medical students and assess in how far processes of Othering shape their approach.

This study employed a cross-sectional design using story completion as a means of data collection.

Story completion as a research method

Story completion is a relatively novel qualitative technique. It presents the study participants with the beginning of a story, the story stem . This stem acts as a stimulus intended to elicit a reaction in the participants who are then asked to complete the story [ 53 ].

Originally, the method is rooted in the projective tests developed to access the unconscious, and reveal psychopathological truths of the patients [ 54 ]. The feminist researchers Celia Kitzinger and Deborah Powell first introduced story completion to qualitative research discussing essentialist and constructionist approaches in their research while exploring the social discourses surrounding infidelity in partnerships [ 55 ].

Contrary to the early projective tests, the stimulus in story completion read in a constructionist frame as suggested by Kitzinger and Powell, prompts participants to draw from familiar social discourses in writing their story. The method was further developed by a handful of researchers mostly focusing on the topics of gender, sexuality, and relationships from a feminist perspective [ 56 , 57 , 58 , 59 , 60 , 61 , 62 ] and increasingly in health research as well [ 63 , 64 , 65 , 66 ].

Since it asks participants to write fictitious stories and thus shifts focus to a hypothetical scenario, story completion is especially suited for topics dealing with sensitive and controversial issues, where the social desirability bias might otherwise be particularly strong [ 53 ]. Considering the controversies surrounding refugees in Germany, study participants might be hesitant to disclose their views and attitudes directly, since this debate is often perceived as polarized and emotionally charged. Therefore, story completion offers significant potential in this study that is aiming to explore perception, attitude, and bias towards a marginalized patient population.

Story stems

The story stems were developed to present a familiar, concise scenario that could evolve into a myriad of directions. The patient characteristics vary only slightly between the two stems: the patient is either described simply as a young, male patient without any further ascriptions (an “unmarked” patient) or a young, male patient with a history of refugee migration (a patient “marked” as a refugee). Both stems were piloted with a group of 13 medical students that were from another semester than the ones recruited for the study to avoid data contamination [ 67 ]. After the pilot, the story stems were slightly adjusted, resulting in the following two versions used for the study:

„You are doing a clinical rotation in a primary care practice. On a Monday morning, a 22-year-old patient presents with abdominal pain.”

„You are doing a clinical rotation in a primary care practice. On a Monday morning, a young patient presents with abdominal pain. He is 22 years old and fled to Germany a year ago.”

The participants were given instructions to be as creative as desired. They were asked however, to write at least 10 lines or 200 words. We provided them about 20 min to write the story, fill out a questionnaire on empathy, which will be the focus of a follow-up paper, and answer questions on their demographic characteristics.

Patient A: the norm (?)

The research question as well as the comparative stem design need to be considered within the context of an already established societal framework defining social norms. Even though it would certainly be interesting and necessary, an in-depth discussion of the intricacies of the current scientific debates on questions surrounding the “unmarked norm”, the Subject opposite the Other, is beyond the scope of this article. While we are acutely aware that our comparative study design might reproduce the duality of Othering mechanisms, it allows us to juxtapose both patients, position the unmarked patient A as a point of reference and investigate the narrative construction of our primary group of interest further.

Sampling and data collection

According to previous research, a sample size of at least 10 participants per story stem variation is recommended [ 53 , 68 ]. To provide ample data, we aimed to recruit all 5th -year medical students enrolled in the compulsory class “Introduction to Social Medicine” who attended the class during the data collection, a total of 166 students. To reach a high response, the students were personally approached by the first author and invited to participate during the class, which is taught by the research team, in October 2019. Students were randomly assigned a version. The survey was carried out in a pen-and-paper format.

Demographic properties of the sample

Out of the 166 students that were approached by the researchers, 128 students (77%) agreed to participate. Of these, four participants only filled out the empathy questionnaire and declined to write a story. Thus, out of the 124 stories, version A was completed by 53 participants, version B by 71 participants, one of whom did not write a cohesive story but discontinued mid-sentence.

The mean word count was 132 words with a range of 43 to 245 words per story. Almost all participants ( n  = 115) wrote their stories during the time slot given in the seminar, while nine turned their stories in later. Approximately one third of the participants identified as male ( n  = 43), two thirds ( n  = 81) as female and one as gender diverse. Three participants did not reveal their gender. The age range was 21 to 41 years (median = 24 years); most were between the ages of 22 and 26 years (76%).

To distinguish between the study participants and the fictional students in the stories, in the following we use the term “participants” to refer to the actual students that participated in the study while using the term “students” to refer to the fictional students that appear in the stories.

Qualitative analysis of the story stems

The stories were coded using the software MAXQDA Plus (Version 2020). To prevent bias during the analysis, the stories were anonymized and blinded for the story stems beforehand.

We coded the data using the qualitative content analysis approach by Mayring [ 69 ]. During the process, we focused on promising ideas and patterns, first exploring the stories’ contents: How are the characters portrayed? Which medical conditions are ascribed to the patients and which corresponding etiologies are suggested? How is access to care described? Hereby, especially the depiction of restrictions to adequate care were of interest: Are there any barriers to adequate access? How do the stories deal with potential barriers? How do these affect the care patients received? Here, we focus on the depiction of refugee patients receiving substandard medical care.

In a second step, we explored two narrative strategies. Analyzing narratives allowed us to examine a story through a more comprehensive lens and helped us understand the reasons and ways in which the stories are constructed [ 70 ]. First, we were interested in the underlying structure defining the plot, or rephrased: What framework or idea drives the unfolding of the story? Second, we examined the perspective of the story, in this case referring to which characters or aspects are central to the story. That is, who or what do we learn about the most?

To ensure the reliability of our analyses, we assessed the intercoder reliability following Mayring’s guidelines for a discursive approach [ 71 , 72 ]. After an introduction to the categories, the supervising researcher coded a random sample that constituted 10% of the data. Subsequently, we discussed the discrepancies in the codes and resolved them, if possible.

Data interpretation

Following the coding process, we calculated the relative frequencies of the codes using the software MAXQDA Plus. To enable comparisons, descriptive statistics were performed after stratification for the two story stem versions, and for participants’ gender.

To illustrate the categories, we present data extracts from the stories below, identifiable by a code as well as by the patient’s (un-)markedness indicating which version the participant received. They have been translated into English and spelling errors have been corrected to aid readability.

This section will demonstrate the study’s findings according to the main themes of the content analysis: the portrayal of the characters, the depiction of the medical conditions and the access to care as wells as the description of substandard care. In a final section, the narrative strategies will be illustrated.

While most of the stories took place in a real-world setting, two participants displayed their creativity and wrote rather fantastical stories:

„As I pushed more forcefully, suddenly his abdominal wall ruptured and an alien-like creature jumped out towards me. It had a slimy consistency and smelled like rotten eggs. I tried to capture it, but it was moving too fast and disappeared with a leap through the cracked window of the exam room.” (B27) .

Content analysis

Characters.

In most stories, the two main characters were the patient and the medical student. The following section will highlight how they were portrayed differently in the story versions.

The history of refugee migration mentioned in the stem was crucial to the description of the marked patient in the stories. Some participants referred to him as belonging to the groups of “refugees” (B28) or “displaced persons” (B56). Others emphasized that the patients showed up “like every other urgent patient” (B1) and would therefore be treated like any other patient, irrespective of their insurance status as refugees (B18). Similarly, one participant referred to the patient’s origin while describing his appearance simply as “südländisch” (B35), thus implying a certain generalized look due to his “southern” heritage.

In both versions, the patient was generally depicted as being open-minded, studious, and polite. Some version B stories however, specifically emphasized this and were “really impressed [by] his open and polite nature” (B21). Others stressed the patients’ cooperativeness and compliance: the refugee patient “tries visibly” (B2) to explain his history, yet still failed “despite all of his efforts” (B63).

While the marked refugee patients were depicted sympathetically throughout the stories, a reluctant or difficult attitude, was notably ascribed only to unmarked patients (in 10 out of 53 stories pertaining to him):

„Since he didn’t have insight into his disease and he only wanted something to counter the pain, it was difficult to convince him of how to proceed.” (A42) .

In most stories the medical students also played an essential role. They were twice as likely to show positive and friendly attitudes towards the refugee patient than towards the unmarked patient. This was apparent in their desire to help (B4), “to calm him down and relieve his anxiety” (B16) or in complimenting his language skills (B48). Some participants described students that were eager to emphasize their wish to help, explaining that the patient’s well-being was dear to their heart (B22).

None of the stories described a deliberately negative attitude of the students towards the patients.

Summing up, noteworthy differences between the two versions include the emphasis put on the marked patients’ history of refugee migration, the consistently positive characterization of the refugee patient in comparison to the unmarked patient as well as the medical students’ sympathetic and compassionate attitude especially towards the marked patient.

Medical condition.

This second main category will take a closer look at the medical condition of the patients in the stories and their underlying etiologies.

It is important to note that the patients did not receive a diagnosis in all stories: In some, this fact was omitted, whereas in others, assigning a diagnosis to the patient played no significant role for the storyline. Interestingly, more than 80% of the patients in the unmarked version A were assigned a diagnosis, compared to roughly 50% of the marked patients in version B. Overall, appendicitis was the most common diagnosis. However, this was not the predominant diagnosis in version A stories. Here, more patients, approximately one-fifth, were suffering due to psychosomatic abdominal pain.

Oftentimes, more complex storylines lead up to these diagnoses and upon examination of the underlying etiologies, with an interesting difference between the two groups. In version A, the etiologies were largely grounded in the patients’ professional lives: All psychosomatic diagnoses, as well as four other diagnoses, were explained by the fact that the unmarked patients were experiencing a stressful situation at their job, school, or university.

For the marked patients however, the etiologies were consistently contextualized within their migration experience. The psychosomatic abdominal pain diagnosed in the six patients in version B was solely grounded in the patients’ status as refugees, the trauma experienced while fleeing their home country and the ensuing difficulties faced in Germany:

“In addition, he is under severe psychological pressure, because his family still lives in his home country and he is very worried.” (B24) .

One of the refugee patients was additionally diagnosed with depression “because the memories still haunt him” (B4). Furthermore, one story even painted a picture of repeated abuse of refugees by security guards at the shelter for asylum seekers:

„Many of his roommates seem to be also exposed to regular physical abuse.” (B20) .

Finally, other stories suggested quite a different scenario: Several patients were accused of or diagnosed with feigning their condition. The unmarked patients in version A did so hoping to receive a doctor’s note for sick leave from work, school or university. In the version B group, one patient was accused of faking after having difficulties communicating with the doctor:

“After a very quick physical exam, the doctor can’t find critical evidence. According to the doctor, the history consists of rather vague, sometimes contradicting, statements. The doctor is inclined to dismiss the patient as a ‘faker.’” (B75) .

Another patient did so supposedly with the intention to affect asylum claims:

“He doesn’t have any complaints and only wants to avoid work or being deported!” (B1) .

Overall, the analysis of the medical conditions suggests a particular perception of the patients: the unmarked patients were more likely to be assigned a diagnosis at all, to be diagnosed with psychosomatic abdominal pain and to be accused of feigning. Job and school related issues were the predominant underlying reasons in this group. In contrast, those refugee patients whose stories included a diagnosis or etiology, were predominantly depicted as suffering under migration-related trauma.

Access to care.

The main barrier impacting access to care in the stories was communication: More than half of the participants writing stories with refugee patients stated language problems, whereas none in the version A stories described any barrier. While in some stories the language barrier was only stated as such, many stories (41%) elaborated on the matter of communication and provided one specific reason for language barriers, namely the patient’s lack of speaking German:

„Unfortunately, there’s a big language barrier because the patient doesn’t speak German well yet.” (B10) .

Two stories were even more explicit: Refugees were specifically blamed for lacking knowledge of the language by the doctor in the stories.

„It’s always the same! Learn German!“ (B1) .

However, another two participants also pointed towards other reasons such as the doctors’ inability to speak English, delayed access to German language classes for the patients and their recent arrival to Germany:

„Even though the family practitioner studied English in school, she hasn’t spoken regularly in a long time. Therefore, she had problems communicating with the young man whose German isn’t all too good yet, because it took a couple of months until he received access to language classes.” (B59) .

These hurdles in communication between the characters oftentimes shaped the clinical interaction and resulted in insufficient history taking. However, almost 40% of the stories outlined an effort to resolve that barrier by engaging an interpreter, switching to English or using gestures and facial expressions. In one story, the student even distributed a brochure in Arabic (B36). These efforts did not always turn out to be successful though. In others yet, the doctor or the medical personnel refused to try alternative ways in communicating with the patients in the first place:

„The doctor didn’t try to explain some things in English, but instead quickly brushed aside, everything that wasn’t understood.” (B34) .

In contrast to the stories highlighting language barriers, eight specifically emphasized the lack thereof and pointed out the patients’ ability to speak German sufficiently. In one story, the student even fondly expressed their surprise about the patient speaking fluently:

„I was really surprised when he said [he has been in Germany for] only 1 year and [he has] also only been studying German since then – he was already really fluent.” (B50) .

Overall, stories concerning the refugee patient overwhelmingly engage the problem of language-related problems, one way or another. These and other access barriers were a prevalent theme of interest solely in stories with refugee patients – some resulting in substandard care as shown in the following section.

Provision of substandard care.

Almost one-fifth of the stories in the marked version B portrayed a situation with insufficient conditions for the patient in which he received substandard care (such as inadequate history taking, lack of diagnosis or lack of adequate treatment). Most stated an unresolved language barrier as the primary reason, some however also cited a stressful environment and work schedule, general prejudice towards refugees and lack of patience of the medical personnel.

The stories dealt with these discriminatory situations very differently. In five stories, the students addressed the inadequacy of the situation and outlined different narratives concerning the lack of equal access to care. Four of them clearly denounced it:

„I’m sitting helplessly on the side, trying to help with English/gestures/facial expressions from time to time. At which point however the doctor is signaling nonverbally that my actions are not appreciated. After taking a rather bad history, the doctor examines the patient clinically.” (B34) .

Three qualified the discriminatory actions in the context of a stressful work environment and well-meaning doctors:

“Due to the other days during this internship, I don’t believe the doctor is racist, but rather ‘just’ stressed.” (B34) .

Furthermore, some students reflected on the confusion, helplessness, and internal conflict regarding their own role:

“Am I naïve because I give everyone a chance anyway? Will I lose my patients later too when there’s not enough time? How are you supposed to solve these problems that start in their heads and are deeply engrained even though someone has ‘just’ stomach pain?” (B1) . „This helplessness and powerlessness is difficult to endure and I am slowly beginning to be able to understand the family practitioner a little bit.” (B75) .

Especially narratives detailing the provision of substandard care to the patients explored a recurring theme: Faced with discrimination of the patients, the students are oftentimes portrayed as compassionate yet helpless mediators next to the – in some cases dismissive – doctors.

Yet, it is important to note that more than half of participants describing a discriminatory situation did not specifically address it as such nor discussed underlying reasons or resulting consequences in their writing. In contrast, in stories with the unmarked patients, barriers to care or a lack thereof were not mentioned and not considered as something that might impair the clinical interaction.

In conclusion, access to care or lack thereof, its presumed causes and its reception were a major theme in the stories with a marked patient but not a relevant topic in stories narrating care for unmarked patients.

Narrative strategies

Finally, we shifted the focus of our analysis towards different overarching narrative strategies employed in the stories. This following section will examine first the underlying structure of the plot and secondly the central perspective of the stories. It is important to note that we considered there to be only one central perspective in a story, whereas shifts and breaks in plot structure occurred sometimes and thus more than one plot structure per story is possible.

Plot structure.

Analyzing the plots of the stories, we found two main approaches, a medical and an interaction-based approach to telling the story. In the medical plot, the stories were structured by the typical steps of a patient consultation that usually include taking a history, a physical examination, diagnosis, and treatment options:

„After taking a thorough history, […] I moved on to the clinical examination. […] After auscultation and palpation as well as testing appendicitis signs, I suspect ‘appendicitis’. […] We immediately refer Mr. Schmidt to the closest hospital for the surgery.” (A33) .

More than half of the participants applied such a distinct medical plot to their stories. However, stories in response to the unmarked story stem A tended to do so more frequently than those responding to version B (71% vs. 48%).

The second most used plot structure is based on the elaboration of interaction between the characters. This interaction can be constituted by verbal and non-verbal communication unrelated to strict medical history taking as well as actions that allow conclusions to the characters’ relationship. Here, the way the characters played off each other was the main driving force for the plot:

“And then I saw it again, the wondrous transformation happening to the otherwise very correct, usually somewhat ironically-distanced German doctor. He released an avalanche of French kind remarks onto the patient, asked first of all about the family, the advantages of Cameroonian beer and laughed and joked until the grey examination room disappeared into the background and someone displaced found again some comfort. I was sitting on the side, understood only half and yet learned so much.” (B56) .

Such an interaction-based plot was more prevalent in the version B stories with a marked patient compared to version A (42% vs. 30%) and largely prompted by a migration-related element (such as experiences during the process of migration or language and cultural differences) in the story (77%).

Noteworthy is also the association between stories in which the patients were given a diagnosis and the predominant plot structure: While the unmarked patients, as mentioned above, are generally more likely than the marked patients to receive a diagnosis, the difference is even more prominent when comparing stories using an interaction-based plot. Here, 94% of unmarked patients receive a diagnosis compared to 53% of the marked patients.

Perspective.

As a second narrative strategy, we examined the perspective of the stories. That is, we analyzed which characters are central to the stories.

In almost half of the stories, few if any details on the characters were given, no single character stood out and they all remained relatively neutral and bland. Instead, the stories centered around a sequence of actions; we call this type of perspective process-oriented. There were no substantial differences between the versions (45% A vs. 49% B). However, almost 75% of these stories also followed a distinct medical structure. The following example illustrates this common overlap very well:

“After consulting with the family practitioner, I’m allowed to first examine the patient myself in a separate room. So, I ask him to enter, introduce myself and take a history concerning his symptoms.[…]. Following the history taking, which hasn’t provided any critical diagnostic clues, I ask him to lie down on the examination table and examine his abdomen. I notice abdominal guarding in the upper middle abdomen. This is where he indicates to have the most pain. I remember that he said, he had eaten little, and I suspect a gall bladder infection. Following the clinical exam, I share my results with the family practitioner. After doing an ultrasound, the sonographic criteria point towards a gall bladder infection.” (A32) .

Other stories, approximately one-fifth, were centered around the patient as the protagonist (21% A vs 24% B). While the other characters tended to appear only marginally, these stories included vivid details on the patients’ characteristics, experiences, or emotions:

„She enters the doctor’s office with her head down, yet she is wearing very colorful clothes, many necklaces around her neck, earrings and bracelets. After noticing us, she raises her head and smiles at us.” (A5) .

In some stories (17% A vs 14% B), the first-person narrators acted as the main protagonists and presented a rather intimate perspective in sharing their thoughts and emotions:

„Everything I’m hearing from Mr. W., makes me very sad and angry. It’s insane what people can endure without going crazy. I stop for a second and think of my life. How good do I have it!” (A52) .

Few stories however also focused on a rather unusual “character”: the patients’ medical condition (17% A vs 7% B). In those cases, many clinical details on the patients’ symptoms and medical history were revealed:

„The patient presents in a reduced overall state, fever of 41°C and very strong abdominal pain. […]The patient’s heart rate is 120/min and his blood pressure is 90/60. I examine him clinically and notice abdominal guarding as well as very strong generalized abdominal tenderness.” (A14) .

Overall, the influence of the two story stem versions was most salient in the comparison of plot structures. The use of a medical plot was predominant in the version A stories with an unmarked patient, focusing the narrative on a rather straightforward exchange of medical information. The interaction-based plot, on the other hand, which is more common with version B stories, shifted the narrative towards an exchange mostly prompted by the patients’ refugee status and strongly emphasizing the characters’ relationships. Considering the perspectives, the stories frequently featured a clear sequence of actions with the differences between the two versions remaining rather modest. Yet it is noteworthy that the use of this process-oriented perspective coincided most with a medical plot.

Summary of results

In conclusion, these results show the key role the patients’ refugee status seemed to play in the construction of the stories. The medical condition, access to care as well as the resulting substandard care are largely contextualized within the patients’ history of migration. In contrast, the stories relating to the unmarked patient do not touch upon such context factors and tend to focus strictly on biomedical topics. Furthermore, the patient’s refugee status prompted an especially empathic depiction of the patients. However, not only the content of the stories is centered on the patients’ history of migration, the manner in which the stories are constructed differs considerably. The empathic depiction is driven by the prevalent use of an interaction-based plot which is rare in the stories relating to non-migrant patients. Providing a fitting framework for the development of the stories, the use of narrative strategies highlights the one-dimensional lens where patients’ migration experience overshadows other (potential) patient characteristics.

In the discussion, we will now delve further into the context of these results and embed them into the theory of Othering.

When applying a constructionist framework to story completion – as suggested by Kitzinger and Powell – it allows us to draw conclusions about “contemporary discourses upon which subjects draw in making sense of experience” [ 55 ]. Therefore, our data speaks to the construction of refugee patients in the discourses to which the participants are exposed. It sheds light on how refugee patients are talked and thought about in this particular space. In the following section, this paper will therefore discuss through a postcolonial feminist lens how the construction of “the refugee patient” in this study constitutes a form of Othering.

Othering: perception through a narrow lens

Building on the theoretical background outlined in the introduction, we would like to argue that overall, in the analyzed stories, the patients in version A represent the default, the unmarked norm, and patients in version B the Other. The one factor defining the marked patient is his status as refugee.

Whereas he is also characterized as being male and young, his migration status is predominant in determining his characters’ description as well as his interactions with other characters. While this focus might certainly be partly due to the reproduction of the marker “refugee” in the story stem, it impacts not only the content of the stories but also shapes the narrative structure in a remarkable way.

Being categorized as a refugee overshadows all other possible markers of identity: Neither gender nor age are utilized in such an instrumental fashion. The relevance and key role that is attributed to the patient’s Otherness, his belonging to a specific group – refugees – sets him apart from the unmarked patient. This narrow lens of perception acts as a backdrop for the following considerations on the intricacies of Othering mechanisms in the stories.

Hereby, it is noteworthy that the one-dimensional perspective on the patient marked as “refugee” elicited mostly compassionate, empathic, and more humane approaches towards this patient. While the unmarked patients in the stories were cared for according to the textbook, engagement with the refugee patients was more individualistic, and less structured by medical reasoning. While at first glance, this seems like a favorable approach towards the delivery of health care, it also entails problems, to which we turn in the sext section.

Othering through compassion

The students’ compassion and explicit concern for the patient is probably the most defining characteristic of stories answering to story stem B. In the following, we want to outline why this raises important questions and signals a deficit of medical education. Hereby, we draw on the debate around the concept of “othering through compassion”.

Othering through compassion describes putative benevolent attitudes towards a group of people, whose social position is typically lower (or marginalized in other ways) compared to the position of the person performing the Othering.

The idea of compassion in general is defined by Didier Fassin as “sympathy felt for the misfortune of one’s neighbor [that] generates the moral indignation that can prompt action to end it” [ 49 ]. The students’ compassion for the refugee patient (but not the unmarked patient) as well as their will to act accordingly is evident in the stories, yet it must be considered within the context of an unequal society. While the moral sentiment itself is inherently one of solidarity and acts of compassion naturally intend to strive for equality, they are overwhelmingly only directed towards the vulnerable and destitute, the less powerful.

In this context, the students’ benevolent attitude towards the patient can therefore be seen as an expression of hierarchy. Their felt obligation to engage the patient “as refugee” is innately rooted in asymmetrical societal power relations. Fassin refers to this as “politics of compassion” [ 49 ]. Moreover, the symbolic power and identity building attached to the generosity of providing care to vulnerable humans is another aspect worth highlighting in this context: It entails the construction of oneself as a charitable Subject caring for a vulnerable but deserving Other [ 32 ].

In addition to the inequalities inherently implicated in the concept of compassion, it does not necessarily result in constructive action resolving the observed conflict. The recognition of a double standard in the provision of care leaves the students in the story feeling helpless and powerless since their attempts to overcome these difficulties in every-day clinical practice mostly remained futile. In the face of structural inequities and systematic barriers to care, they seem to withdraw to a position of sympathy and compassion.

Compassion alone, however, appears to distract from the recognition of the structural political, social, and economic factors affecting the patients’ care. Compassion thus remains a reductionist and inept response without the tools for a constructive examination of the underlying discriminatory structures. Suggesting an insufficient preparation in recognizing and dealing with these structural determinants of health during their medical education, the students in the stories were not able to translate their compassion into the effective tools needed. A reflection of societal inequality, the sentiment of compassion simultaneously perpetuates discriminatory and unequal situations by ignoring their structural underpinnings.

Since the stories take place in a fictional realm, our analysis does not allow for conclusions about the actual perceptions and actions of the study participants. Yet, they point towards a discourse in which refugee patients continue being subjected to a double standard in care while being compassionately treated like a marginalized Other. The largely uncritical reproduction of this discourse in the stories, however, raises the need for a critical examination of this discursive patterns as well as the sensitization of health care providers.

While here the stories do not speak to compassion as a fruitful foundation for change, when dealt with accordingly, it may serve still as a starting point to constructive change. Compassion may spark a conversation about Othering and action to address these health inequalities.

Methodological reflections and limitations

The story completion method has been a fruitful and insightful method for this study. However, it is crucial to acknowledge that it aims to examine discursive phenomena rather than actual perceptions and actions of the study participants. Thus, our finding of “compassionate othering” clearly speaks to the nature of the discourses medical students are exposed to and draw from when writing their stories, but it does not necessarily reflect how participants would act if they were in the position of the student in the story.

Furthermore, while the socially value-laden and structurally important category of “migration” has prompted ample data, the lack of a designated category for patient A may have prompted the coherent use of a standard medical narrative in this version. Marking the second patient with an uncontroversial attribute, such as a hobby, might have provided a more diverse set of data. Still, story completion holds a lot of potential and this study aims to contribute to its further methodological advancement.

The discourse that shaped the respondents’ stories takes place in a system that still systematically lacks attention to the structures producing health inequities. With much of medical education focused on a biomedical paradigm, students and medical practitioners alike fall back on every-day discourses and their gut feeling when confronted with patients that (seem to) require a more biopsychosocially informed approach [ 73 ]. Compassion then is not translated into action that challenges the forces creating structural vulnerability for certain types of patients but materializes in the form of intrapsychic conflict.

Yet, universities are in a pivotal position to start adequately preparing future medical professionals to care for the structural vulnerability of marginalized patients [ 74 , 75 , 76 ]. In order to be able to provide the best care possible, students need to be trained to recognize the various factors contributing to inequities and be equipped with tools to navigate care for their patients [ 77 , 78 ]. Hereby, a critical reflection of one’s own role within the overarching power dynamics should be an essential first step to reflect on the question why certain patient characteristics engender compassion (and others don’t). As our analysis highlighted, the discourse shaping our respondents’ stories is still strongly influenced by Othering mechanisms in the perception of refugee patients and therefore stresses the importance of structural competency training during medical education.

Data availability

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

Bundesamt für Migration und Flüchtlinge, Hrsg. Das Bundesamt in Zahlen 2018. Nürnberg; April 2019. Verfügbar unter: http://www.bamf.de/SharedDocs/Anlagen/DE/Publikationen/Broschueren/bundesamt-in-zahlen-2018-asyl.html?nn=9121126 .

Razum O, Wenner J, Bozorgmehr K. Wenn Zufall über den Zugang Zur Gesundheitsversorgung bestimmt: Geflüchtete. Deutschland Gesundheitswesen. 2016;78(11):711–4. Verfügbar unter:. http://www.thieme-connect.com/products/ejournals/pdf/10.1055/s-0042-116231.pdf .

Article   Google Scholar  

Spura A, Kleinke M, Robra B-P, Ladebeck N. Wie Erleben Asylsuchende den Zugang zu Medizinischer Versorgung? Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2017;60(4):462–70. https://doi.org/10.1007/s00103-017-2525-x .

Hall WJ, Chapman MV, Lee KM, Merino YM, Thomas TW, Payne BK, et al. Implicit Racial/Ethnic Bias among Health Care Professionals and its influence on Health Care outcomes: a systematic review. Am J Public Health. 2015;105(12):e60–76. https://doi.org/10.2105/AJPH.2015.302903 .

Karger A, Lindtner-Rudolph H, Mroczynski R, Ziem A, Joksimovic L. „Wie Fremd Ist Mir Der Patient? Z Psychosomat Med Psychother. 2017;63(3):280–96. Verfügbar unter:. https://www.researchgate.net/publication/320203331_Wie_fremd_ist_mir_der_Patient_Erfahrungen_Einstellungen_und_Erwartungen_von_Arztinnen_und_Arzten_bei_der_Versorgung_von_Patientinnen_und_Patienten_mit_Migrationshintergrund .

Google Scholar  

Bischoff A, Hudelson P. Access to healthcare interpreter services: where are we and where do we need to go? Int J Environ Res Public Health. 2010;7(7):2838–44. Verfügbar unter. https://www.mdpi.com/1660-4601/7/7/2838 .

Flores G. Culture and the patient-physician relationship: achieving cultural competency in health care. J Pediatr. 2000;136(1):14–23. Verfügbar unter:. https://pubmed.ncbi.nlm.nih.gov/10636968/ .

Lindert J, Priebe S, Penka S, Napo F, Schouler-Ocak M, Heinz A. Versorgung psychisch kranker Patienten Mit Migrationshintergrund. Psychother Psychosom Med Psychol. 2008;58(3–4):123–9. https://doi.org/10.1055/s-2008-1067360 .

Machleidt W, Behrens K, Ziegenbein M, Calliess IT. Integration Von Migranten in die psychiatrisch-psychotherapeutische Versorgung in Deutschland. Psychiatr Prax. 2007;34(7):325–31. https://doi.org/10.1055/s-2007-986192 .

Führer A, Eichner F, Stang A. Morbidity of asylum seekers in a medium-sized German city. Eur J Epidemiol. 2016;31(7):703–6. https://doi.org/10.1007/s10654-016-0148-4 .

Hadgkiss EJ, Renzaho AMN. The physical health status, service utilisation and barriers to accessing care for asylum seekers residing in the community: a systematic review of the literature. Australian Health Review: Publication Australian Hosp Association. 2014;38(2):142–59. Verfügbar unter:. https://pubmed.ncbi.nlm.nih.gov/24679338/ .

Tallarek M, Bozorgmehr K, Spallek J. Towards inclusionary and diversity-sensitive public health: the consequences of exclusionary othering in public health using the example of COVID-19 management in German reception centres and asylum camps. BMJ Glob Health. 2020;5(12). https://doi.org/10.1136/bmjgh-2020-003789 .

Petros G, Airhihenbuwa CO, Simbayi L, Ramlagan S, Brown B. HIV/AIDS and ‘othering’ in South Africa: the blame goes on. Cult Health Sex. 2006;8(1):67–77. https://doi.org/10.1080/13691050500391489 .

Johnson JL, Bottorff JL, Browne AJ, Grewal S, Hilton BA, Clarke H. Othering and being othered in the context of health care services. Health Commun. 2004;16(2):255–71. https://doi.org/10.1207/S15327027HC1602_7 .

Kang E, Rapkin BD, Springer C, Kim JH. The Demon Plague and access to care among Asian undocumented immigrants living with HIV disease in New York City. J Immigr Health. 2003;5(2):49–58. Verfügbar unter:. https://link.springer.com/article/10.1023/A:1022999507903 .

Levinas E. Totality and infinity. Dordrecht: Springer Netherlands; 1979.

Book   Google Scholar  

Bachtin M, Holquist M, Liapunov V. Toward a philosophy of the act. 3., paperback printing. University of Texas Press Slavic series. Volume 10. Austin: Univ. of Texas; 1999.

Rieber RW, Carton AS. The Collected Works of L. S. Vygotsky. Boston, MA: Springer US; 1988.

de Beauvoir S, Borde C, Malovany-Chevallier S. The second sex. New York: Knopf Doubleday Publishing Group; 2012.

Said EW, Orientalism. Western conceptions of the orient. Reprinted with a new afterword. London: Penguin Books; 1995. (Penguin history).

Spivak GC. In other worlds: Essays in cultural politics. 2nd ed., reprint. New York: Routledge; 2008. (Routledge Classics). Verfügbar unter: http://gbv.eblib.com/patron/FullRecord.aspx?p=1099050 .

Spivak GC. The Rani of Sirmur: an essay in reading the archives. History Theory. 1985;24(3):247. https://doi.org/10.2307/2505169 .

Spivak GC. Can the subaltern speak? Postkolonialität und subalterne Artikulation. Unveränd. Nachdr. [Place of publication not identified]. Es kommt darauf an. Volume 6. Verfügbar unter: TURIA & KANT; 2014. http://deposit.d-nb.de/cgi-bin/dokserv?id=3022949&prov=M&dok_var=1&dok_ext=htm .

Landry D, MACLEAN G, Spivak GC, editors. The Spivak reader: selected works of Gayatri Chakravorty Spivak. Transferred to digital printing 2010. New York: Routledge; 1996. (Literary criticism). https://books.google.de/books?id=Id2NAQAAQBAJ .

Powell JA, Menendian S. The Problem of Othering: towards inclusiveness and belonging. Othering Belonging. 2016;1(1):14–39. [Stand: 16.01.2021]. Verfügbar unter:. http://www.otheringandbelonging.org/the-problem-of-othering/ .

Jensen SQ. Othering, identity formation and agency. QS. 2011;2(263–78. https://doi.org/10.7146/qs.v2i2.5510 .

Becht L, Boucsein L, Mayr K. The dynamics of othering in activism as part of Germany’s post-2015 Willkommenskultur. Dve domovini - Two Homelands. 2018;47:53–70.

Udah H, Singh P, Identity. Othering and belonging: toward an understanding of difference and the experiences of African immigrants to Australia. Social Identities. 2019;25(6):843–59. https://doi.org/10.1080/13504630.2018.1564268 .

Grove NJ, Zwi AB. Our health and theirs: forced migration, othering, and public health. Soc Sci Med. 2006;62(8):1931–42. Verfügbar unter:. http://www.sciencedirect.com/science/article/pii/S0277953605004806 .

Crawley H, Skleparis D. Refugees, migrants, neither, both: categorical fetishism and the politics of bounding in Europe’s ‘migration crisis’. J Ethnic Migration Stud. 2018;44(1):48–64. https://doi.org/10.1080/1369183X.2017.1348224 .

Wenner J, Namer Y, Razum O, Migrants. Refugees, Asylum Seekers: use and Misuse of Labels in Public Health Research. In: Krämer, editor. Refugee Migration and Health. [Place of publication not identified]. 1st ed. Springer International Publishing; 2019. pp. 49–62. (Migration, Minorities and Modernity).

Olsen C, El-Bialy R, Mckelvie M, Rauman P, Brunger F. Other troubles: deconstructing perceptions and changing responses to refugees in Canada. J Immigr Health. 2016;18(1):58–66. https://doi.org/10.1007/s10903-014-9983-0 .

Flowers P. Gay men and HIV/AIDS risk management. Health (London). 2001;5(1):50–75. https://doi.org/10.1177/136345930100500103 .

Chapman J, Estcourt CS, Hua Z. Saving ‘face’ and ‘othering’: getting to the root of barriers to condom use among Chinese female sex workers. Sex Health. 2008;5(3):291–8. https://doi.org/10.1071/sh07057 .

de Vries DH, Galvin S, Mhlanga M, Cindzi B, Dlamini T. Othering the health worker: self-stigmatization of HIV/AIDS care among health workers in Swaziland. J Int AIDS Soc. 2011;14:60. https://doi.org/10.1186/1758-2652-14-60 .

Flowers P, Langdridge D. Offending the other: deconstructing narratives of deviance and pathology. Br J Soc Psychol. 2007;46(Pt 3):679–90. https://doi.org/10.1348/014466607X177713 . discussion 691-5.

Schmidt T, Cloete A, Davids A, Makola L, Zondi N, Jantjies M. Myths, misconceptions, othering and stigmatizing responses to Covid-19 in South Africa: a rapid qualitative assessment. PLoS ONE. 2020;15(12):e0244420. https://doi.org/10.1371/journal.pone.0244420 .

Søraa RA, Manzi F, Kharas MW, Marchetti A, Massaro D, Riva G, et al. Othering and Deprioritizing older adults’ lives: ageist discourses during the COVID-19 pandemic. Eur J Psychol. 2020;16(4):532–41. https://doi.org/10.5964/ejop.v16i4.4127 .

van Scoy LJ, Snyder B, Miller EL, Toyobo O, Grewal A, Ha G, et al. Us-Versus-them’: Othering in COVID-19 public health behavior compliance. PLoS ONE. 2022;17(1):e0261726. https://doi.org/10.1371/journal.pone.0261726 .

Demissie M, Hanlon C, Ng L, Fekadu A, Mayston R. Why doesn’t God say enough? Experiences of living with bipolar disorder in rural Ethiopia. Soc Sci Med. 2021;270:113625. https://doi.org/10.1016/j.socscimed.2020.113625 .

Doyle PJ, Rubinstein RL. Person-centered dementia care and the cultural matrix of othering. Gerontologist. 2014;54(6):952–63. https://doi.org/10.1093/geront/gnt081 .

Maccallum EJ. Othering and psychiatric nursing. J Psychiatr Ment Health Nurs. 2002;9(1):87–94. https://doi.org/10.1046/j.1351-0126.2001.00449.x .

Peternelj-Taylor C. An exploration of othering in forensic psychiatric and correctional nursing. Can J Nurs Res. 2004;36(4):130–46.

Walsh D, Foster J. A Contagious Other? Exploring the Public’s Appraisals of Contact with ‘Mental Illness’. Int J Environ Res Public Health. 2020;17(6). https://doi.org/10.3390/ijerph17062005 .

Canales MK, Othering. Toward an understanding of difference. ANS Adv Nurs Sci. 2000;22(4):16–31. https://doi.org/10.1097/ANS.0b013e3181c9e119 .

Malkki LH. Speechless emissaries: refugees, Humanitarianism, and Dehistoricization. Cult Anthropol. 1996;11(3):377–404. https://doi.org/10.1525/can.1996.11.3.02a00050 .

Ticktin MI. Casualties of care: Immigration and the politics of humanitarianism in France. 2. print. Berkeley: Univ. of California Press; 2012.

Ticktin M, Transnational, Humanitarianism. Annu Rev Anthropol. 2014;43(1):273–89. https://doi.org/10.1146/annurev-anthro-102313-030403 .

Fassin D. Humanitarian reason: a moral history of the present times. Berkeley: University of California Press; 2012. Verfügbar unter:. https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=390459 .

Fassin D. Inequalities of life, hierachies of humanity: Moral commitments and ethical dilemmas of humanitarianism. In: Feldman I, Ticktin M, Malkki LH, Hardin R, Wilson RA, editors. The name of humanity: the government of threat and care. Durham: Duke University Press; 2010. pp. 238–55.

Chapter   Google Scholar  

Führer A. „Da Muss Sich Jemand anders kümmern – die medizinische Versorgung Von Asylsuchenden als Herausforderung für eine bio-psycho-soziale Medizin. Gesundheitswesen. 2020;82(2):151–6. https://doi.org/10.1055/a-1026-6190 .

Scott P. Black African asylum seekers’ experiences of health care access in an eastern German state. Int J Migration Health Social Care. 2014;10(3):134–47. https://doi.org/10.1108/IJMHSC-11-2013-0043 .

Braun V, Clarke V, Hayfield N, Moller N, Tischner I. Qualitative story completion: a method with Exciting Promise. In: Liamputtong P, editor. Handbook of Research Methods in Health Social Sciences. Singapore: Springer Singapore; 2018. pp. 1–18.

Rabin AI, editor. Assessment with projective techniques: a concise introduction. New York: Springer; 1981.

Kitzinger C, Powell D. Engendering infidelity: Essentialist and Social Constructionist readings of a Story Completion Task. Feminism Psychol. 1995;5(3):345–72.

Braun V, Clarke V, Hayfield N, Frith H, Malson H, Moller N, et al. Qualitative story completion: possibilities and potential pitfalls. Qualitative Res Psychol. 2019;16(1):136–55. https://doi.org/10.1080/14780887.2018.1536395 .

Clarke V, Braun V, Frith H, Moller N. Editorial introduction to the Special Issue: using Story Completion methods in qualitative research. Qualitative Res Psychol. 2019;16(1):1–20. https://doi.org/10.1080/14780887.2018.1536378 .

Clarke V, Braun V, Wooles K. Thou shalt not covet another man? Exploring constructions of same-sex and different-sex infidelity using story completion. J Community Appl Social Psychol. 2014;25(2):153–66.

Clarke V, Braun V. How can a heterosexual man remove his body hair and retain his masculinity? Mapping stories of male body hair depilation. Qualitative Res Psychol. 2019;16(1):96–114. https://doi.org/10.1080/14780887.2018.1536388 .

Frith H. Accounting for orgasmic absence: exploring heterosex using the story completion method. Psychol Sexuality. 2013;4(3):310–22. https://doi.org/10.1080/19419899.2012.760172 .

Tischner I. Tomorrow is the start of the rest of their life — so who cares about health? Exploring constructions of weight-loss motivations and health using story completion. Qualitative Res Psychol. 2019;16(1):54–73. https://doi.org/10.1080/14780887.2018.1536385 .

Shah-Beckley I. Therapists’ and non-therapists’ constructions of heterosex:: a comparative story completion study. Bristol: University of the West of England; 2016.

Urry K, Hunter S, Feo R, Scholz B. Qualitative story completion: opportunities and considerations for Health Research. Qual Health Res. 2023;10497323221150130. https://doi.org/10.1177/10497323221150130 .

Vaughan P, Lenette C, Boydell K. This bloody rona!‘: using the digital story completion method and thematic analysis to explore the mental health impacts of COVID-19 in Australia. BMJ Open. 2022;12(1):e057393. Verfügbar unter:. https://pubmed.ncbi.nlm.nih.gov/35039307/ .

Braun V, Clarke V, Moller N. Pandemic tales. In: Kara H, Khoo S, editors. Researching in the age of COVID-19. First edition. Bristol: Policy; 2020. pp. 39–48.

Diniz E, Castro P, Bousfield A, Figueira Bernardes S. Classism and dehumanization in chronic pain: a qualitative study of nurses’ inferences about women of different socio-economic status. Br J Health Psychol. 2020;25(1):152–70. https://doi.org/10.1111/bjhp.12399 .

Leon AC, Davis LL, Kraemer HC. The role and interpretation of pilot studies in clinical research. J Psychiatr Res. 2011;45(5):626–9. https://doi.org/10.1016/j.jpsychires.2010.10.008 .

Braun V, Clarke V. Successful qualitative research: a practical guide for beginners. First published. Los Angeles, London, New Delhi: SAGE; 2013.

Mayring P. Qualitative Inhaltsanalyse: Grundlagen und Techniken. 12., überarb. Aufl. Weinheim: Beltz; 2015. (Beltz Pädagogik). Verfügbar unter: http://content-select.com/index.php?id=bib_view&ean=9783407293930 .

Gibbs G, Flick U. Analyzing qualitative data. Los Angeles: SAGE; 2007. (The Sage qualitative research kit/ ed. by Uwe Flick; Pt. 6).

Mayring P. Neuere Entwicklungen in der qualitativen Forschung und der Qualitativen Inhaltsanalyse. In: Mayring P, Gläser-Zikuda M, Hrsg. Die Praxis der qualitativen Inhaltsanalyse. 2. Auflage. Weinheim, Basel: Beltz Verlag; 2008. S. 7–19 (Pädagogik).

Mayring P. Qualitative content analysis: Theoretical Foundation, Basic procedures and Software Solution. Klagenfurt, Österreich: Beltz; 2014.

Engel GL. The need for a new medical model: a challenge for biomedicine. Science. 1977;196(4286):129–36. https://doi.org/10.1126/science.847460 .

Holmes SM. The clinical gaze in the practice of migrant health: Mexican migrants in the United States. Soc Sci Med. 2012;74(6):873–81. https://doi.org/10.1016/j.socscimed.2011.06.067 .

Metzl JM, Hansen H. Structural competency: theorizing a new medical engagement with stigma and inequality. Soc Sci Med. 2014;103:126–33. https://doi.org/10.1016/j.socscimed.2013.06.032 .

Neff J, Holmes SM, Knight KR, Strong S, Thompson-Lastad A, McGuinness C, et al. Structural competency: curriculum for medical students, residents, and Interprofessional teams on the structural factors. That Produce Health Disparities MedEdPORTAL. 2020;16:10888. https://doi.org/10.15766/mep_2374-8265.10888 .

Carruth L, Martinez C, Smith L, Donato K, Piñones-Rivera C, Quesada J. Structural vulnerability: migration and health in social context. BMJ Glob Health. 2021;6(Suppl 1):e005109. Verfügbar unter. https://gh.bmj.com/content/6/Suppl_1/e005109 .

Bourgois P, Holmes SM, Sue K, Quesada J. Structural vulnerability: operationalizing the Concept to address Health disparities in Clinical Care. Acad Med. 2017;92(3):299–307. https://doi.org/10.1097/ACM.0000000000001294 .

Download references

Acknowledgements

The authors would like to thank Christiane Vogel for supporting the pretest of the story stems, Irmgard Tischner for commenting on early versions of the study design and Friederike Eichner whose feedback proved to be extremely helpful.

During the work on this article, AF received funding from Wilhelm-Roux-Program (FKZ 31/29).

Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and affiliations.

Institute for Medical Epidemiology, Biometrics, and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin Luther University Halle-Wittenberg, Magdeburger Straße 8, 06112, Halle (Saale), Germany

Lena Bauer, Andreas Wienke & Amand Führer

You can also search for this author in PubMed   Google Scholar

Contributions

LB designed the study, analyzed and interpreted the data and wrote the manuscript. AF was a major contributor in the design of the study as well as the writing and revision of the manuscript. AW was a major contributor in the design of the study. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Lena Bauer .

Ethics declarations

Ethics approval and consent to participate.

The ethics commission of the Martin-Luther-University of Halle-Wittenberg approved this study on July 25th, 2019. Informed consent was obtained from all study participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Bauer, L., Wienke, A. & Führer, A. Compassionate Othering: the construction of refugee patients in medical students’ narratives – a qualitative study using story completion . BMC Med Educ 24 , 703 (2024). https://doi.org/10.1186/s12909-024-05684-9

Download citation

Received : 17 August 2023

Accepted : 20 June 2024

Published : 27 June 2024

DOI : https://doi.org/10.1186/s12909-024-05684-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Story completion
• Medical students
• Structural vulnerability
• Medical education

BMC Medical Education

ISSN: 1472-6920

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Open access
• Published: 25 June 2024

Achieving research impact in medical research through collaboration across organizational boundaries: Insights from a mixed methods study in the Netherlands

• Jacqueline C. F. van Oijen   ORCID: orcid.org/0000-0002-5100-0671 1 ,
• Annemieke van Dongen-Leunis 1 ,
• Jeroen Postma 1 ,
• Thed van Leeuwen 2 &
• Roland Bal 1  

Health Research Policy and Systems volume  22 , Article number:  72 ( 2024 ) Cite this article

59 Accesses

Metrics details

In the Netherlands, university medical centres (UMCs) bear primary responsibility for conducting medical research and delivering highly specialized care. The TopCare program was a policy experiment lasting 4 years in which three non-academic hospitals received funding from the Dutch Ministry of Health to also conduct medical research and deliver highly specialized care in specific domains. This study investigates research collaboration outcomes for all Dutch UMCs and non-academic hospitals in general and, more specifically, for the domains in the non-academic hospitals participating in the TopCare program. Additionally, it explores the organizational boundary work employed by these hospitals to foster productive research collaborations.

A mixed method research design was employed combining quantitative bibliometric analysis of publications and citations across all Dutch UMCs and non-academic hospitals and the TopCare domains with geographical distances, document analysis and ethnographic interviews with actors in the TopCare program.

Quantitative analysis shows that, over the period of study, international collaboration increased among all hospitals while national collaboration and single institution research declined slightly. Collaborative efforts correlated with higher impact scores, and international collaboration scored higher than national collaboration. A total of 60% of all non-academic hospitals’ publications were produced in collaboration with UMCs, whereas almost 30% of the UMCs’ publications were the result of such collaboration. Non-academic hospitals showed a higher rate of collaboration with the UMC that was nearest geographically, whereas TopCare hospitals prioritized expertise over geographical proximity within their specialized domains. Boundary work mechanisms adopted by TopCare hospitals included aligning research activities with organizational mindset (identity), bolstering research infrastructure (competence) and finding and mobilizing strategic partnerships with academic partners (power). These efforts aimed to establish credibility and attractiveness as collaboration partners.

Conclusions

Research collaboration between non-academic hospitals and UMCs, particularly where this also involves international collaboration, pays off in terms of publications and impact. The TopCare hospitals used the program’s resources to perform boundary work aimed at becoming an attractive and credible collaboration partner for academia. Local factors such as research history, strategic domain focus, in-house expertise, patient flows, infrastructure and network relationships influenced collaboration dynamics within TopCare hospitals and between them and UMCs.

Peer Review reports

Introduction

Research collaboration has taken flight worldwide in recent decades [ 1 ], as reflected by the growing number of authors listed on research papers [ 2 , 3 ]. Collaborative research has become the norm for many, if not most, scientific disciplines [ 4 , 5 , 6 , 7 , 8 ]. Several studies have found a positive relationship between collaboration and output [ 9 , 10 , 11 , 12 , 13 ]. Publications resulting from research collaborations tend to be cited more frequently [ 14 , 15 , 16 , 17 , 18 ] and to be of higher research quality [ 5 , 14 , 19 , 20 ]. In particular, international collaboration can lead to more citations [ 17 , 21 , 22 , 23 , 24 ], although there are major differences internationally and between fields [ 25 ]. Moreover, international collaboration is often set as an eligibility requirement for European research grants, which have become necessary as national-level resources dwindle. Funding consortia also encourage and require boundary crossings, such as research collaborations between academia and societal partners. Collaboration within public research organizations and universities further plays a crucial role in the international dissemination of knowledge [ 26 ].

In the medical domain, initiatives have been rolled out in numerous countries to encourage long-term collaboration and the exchange of knowledge and research findings. Each initiative takes a strategic approach to assembling the processes needed to support these exchanges across the boundaries of stakeholder groups. In the Netherlands, medical research has traditionally been concentrated in public academia, especially the university medical centres (UMCs). Increasingly, however, research activities are being undertaken in non-academic teaching hospitals (hereafter, non-academic hospitals), driven by their changing patterns of patient influx. In 2013, a Dutch study based on citation analysis showed that collaboration between UMCs and non-academic hospitals leads to high-quality research [ 27 ]. There was further encouragement for medical research in Dutch non-academic hospitals in 2014, when a 4-year policy experiment, the TopCare program, was launched, with three such hospitals receiving additional funding from the Ministry of Health to also provide highly specialized care and undertake medical research. Funding for this combination of care and research is available for UMCs under the budgetary “academic component” of the Dutch healthcare system. Such additional funds are not available for non-academic hospitals, nor can they allocate their regular budgets to research. In the past, these hospitals managed to conduct research and provide specialized care through their own financial and time investments, or by securing occasional external research funding. The TopCare policy experiment was thus meant to find new ways of organizing and funding highly specialized care and medical research in non-academic hospitals.

Despite the increasing emphasis on research collaboration, we still know little about its impact and how it can be achieved. This study integrates two sides of research collaboration in Dutch hospitals and combines elements of quantitative and qualitative research for a broad (output and impact) and deep (boundary work to achieve collaboration) understanding of the phenomenon. We define research collaboration as collaboration between two or more organizations (at least one being a UMC or non-academic hospital) that has resulted in a co-authored (joint) scientific publication [ 28 ]. The research questions are: How high is the level of collaboration in the Dutch medical research field, what is the impact of collaboration, and how are productive research collaborations achieved?

To answer these questions, we performed mixed methods research in UMCs and non-academic hospitals. To examine the impact of various collaboration models – namely, single institution, national and international – across all eight Dutch UMCs and 28 non-academic hospitals between 2009 and 2018/2019, we conducted a bibliometric analysis of publications and citations. We additionally carried out a similar analysis for the TopCare non-academic hospitals between 2010 and 2016 to examine the effects of collaboration in the two domains funded through the program at each hospital. The latter timeframe was chosen to match the duration of the program, 2014–2018. We further conducted an in-depth qualitative analysis of the organizational boundary work done by two non-academic hospitals participating in the TopCare program to initiate and enhance productive research collaborations around specialized research and care within and between hospitals on a national level. Historically, such endeavours have been predominantly reserved for UMCs. The program was therefore a unique opportunity to examine such boundary work.

Background and theory

The landscape of medical research in the netherlands, collaboration in medical research.

The Netherlands has a three-tiered hospital system: general hospitals (including non-academic hospitals), specialized hospitals focusing on a specific medical field or patient population, and UMCs. Nowadays, there are 7 UMCs, 17 specialized hospitals and 58 general hospitals, of which 26 are non-academic [ 29 ].

UMCs receive special funding (the budgetary “academic component”) for research and oversee medical training programs in their region. Non-academic hospitals do not receive structural government funding for medical research and have less chance of obtaining other funding because they are not formally acknowledged as knowledge-producing organizations. Research has less priority in most of these hospitals than in UMCs. On the introduction of government policies regarding competition in healthcare and the development of quality guidelines emphasizing high-volume treatments, some non-academic hospitals began focusing on specific disease areas, in a bid to distinguish themselves from other hospitals and to perform research in and hence develop more knowledge about these priority areas. This led to a greater concentration of highly specialized care [ 30 ]. Non-academic hospitals have also become important partners in medical research for UMCs due to their large patient volumes.

The TopCare program

To further stimulate research in non-academic hospitals, the Ministry of Health awarded three such hospitals €28.8 million in funding over a 4-year period (2014–2018) to support medical research and specialized care for which they do not normally receive funding [ 31 ]. It should be noted that, in non-academic hospitals, the concept of highly specialized research and care applies not to the entire hospital but rather to specific departments or disease areas. This is why the TopCare non-academic hospitals have been evaluated on the basis of specific domains. The funding recipients were two non-academic hospitals and one specialized hospital. In this article, we focus on UMCs and general non-academic hospitals and therefore excluded the specialized hospital from our analysis. Hospital #1 is the largest non-academic hospital in the Netherlands (1100 beds), even larger than some UMCs. Its fields of excellence (known as “domains”) are lung and heart care. Hospital #2 is a large non-academic hospital (950 beds) that focuses on emergency care and neurology. According to the two hospitals, these four highly specialized care and research-intensive domains are comparable to high-complexity care and research in UMCs [ 31 ].

The TopCare program ran through ZonMw, the Netherlands Organization for Health Research and Development, the main funding body for health research in the Netherlands. ZonMw established a committee to assess the research proposals and complex care initiatives of the participating hospitals and to set several criteria for funding eligibility. One requirement was that participating hospitals had to collaborate with universities or UMCs on research projects and were not allowed to conduct basic research in the context of the program, as this was seen as the special province of UMCs.

Boundary work

In the qualitative part of this study, we analyse the boundary work done by actors to influence organizational boundaries as well as the practices undertaken to initiate or enhance collaboration between TopCare non-academic hospitals and academia (universities and UMCs). We refer to boundary work when actors create, shape or disrupt organizational boundaries [ 32 , 33 , 34 , 35 ]. In particular, boundary work involves opening a boundary for collaboration and creating linkages with external partners [ 36 ]. In this article, we use three organizational boundary concepts – “identity”, “competence” and “power” – out of four presented by Santos and Eisenhardt. These concepts are concerned with fostering collaboration, whereas the fourth is concerned with “efficiency” and is less relevant here. Identity involves creating a reputation for research to become an attractive partner while preserving identity. Competence involves creating opportunities for research, for example, in manpower and infrastructure. Finally, power involves creating a negotiating position vis-à-vis relevant others [ 35 ].

The data for this study consist of different types of analysis: (1) quantitative bibliometric data on the publications and citations of all eight Dutch UMCs and 28 non-academic hospitals, and (2) quantitative bibliometric data on the publications and citations in the four domains of two TopCare non-academic hospitals, qualitative (policy) document analysis and in-depth ethnographic interviews with various actors in the Dutch TopCare program. The quantitative data collected from Dutch UMCs and non-academic hospitals were utilized to contextualize data gathered within the TopCare program. We discuss and explain the data collection and methodology in detail in the two sections below.

Quantitative approach: bibliometric analysis of all 8 Dutch UMCs and 28 non-academic hospitals

Data collection

We performed a bibliometric analysis of the publications of 28 non-academic hospitals and 8 UMCs Footnote 1 in the Netherlands between 2009 and 2018. Data for the study were derived from the Center for Science and Technology Studies’ (CWTS) in-house version of the Web of Science (WoS) database. The year 2009 was chosen because the address affiliations in publications are more accurately defined from this year onward. To examine trends over time, we divided the period 2009–2018/2019 into two blocks of 4 years and an additional year for citation impact measurement (2009–2012/2013 and 2014–2017/2018; see explanation in Appendix 1).

Methodology

The bibliometric analysis includes several bibliometric indicators that describe both the output and impact of the relevant research (Table  5 in Appendix 1). One of the indicators, the mean normalized citation score (MNCS), reveals the average impact of a hospital’s publications compared with the average score of all other publications in that area of research. If the MNCS is higher than 1, then on average, the output of that hospital’s domain is cited more often than an “average” publication in that research area.

To map the ways hospitals cooperate, we follow two lines of analysis. The first is centred around a typology of scientific activities and differentiates between (i) a single institution (SI;  all publications with only one address) and (ii) international collaboration (IC; collaboration with at least one international partner). All other publications are grouped as (iii) national collaboration (NC; collaboration with Dutch organizations only).

The second line is centred around geographical distance and size of collaboration. The geographical distances between each non-academic hospital and each of the eight UMCs were measured in Google Maps. The size of collaboration was measured by counting the joint publications of each non-academic hospital and the eight UMCs. Subsequently, we assessed whether the non-academic hospitals also had the most joint publications with the nearest UMC.

Quantitative and qualitative approach to the two TopCare hospitals and their four domains, the “TopCare program” case study

Quantitative approach

The quantitative approach to the TopCare program relies on a bibliometric analysis of publications within each hospital’s two domains: lung and heart care in TopCare non-academic hospital #1, and trauma and neurology in TopCare non-academic hospital #2. Our bibliometric analysis focused on publications within the four selected TopCare domains between 2010 and 2016, following the same methodology described in the previous section under ‘Data collection’. Each domain provided an overview of its publications. The number of publications produced by the two domains at each TopCare hospital is combined in the results. Although this timeframe differs from the broader analysis of all UMCs and non-academic hospitals, comparing these two periods offers insights into the “representative position” of the two domains of each non-academic hospital participating in the TopCare program, in terms of publications and citations.

Qualitative approach

We took a qualitative approach to analysing the collaborative activities in the two TopCare non-academic hospitals, where each domain has its own leadership arrangements, regional demographic priorities and history of research collaboration [cf. 37 ]. This part of the study consisted of interviews and document analysis.

Ethnographic interviews

Over the course of the 4-year program, J.P. and/or R.B. conducted and recorded 90 semi-structured interviews that were then transcribed. For this study, we used repeated in-depth ethnographic interviews with the main actors in the Dutch TopCare program, which took place between 2014 and 2018. We conducted a total of 27 interviews; 20 of the interviews were with a single person, 5 with two persons, and 2 with three persons. The interviews were held with 20 different respondents; 12 respondents were interviewed multiple times. Table 1 presents the different respondents in non-academic hospitals #1 and #2.

Document analysis

Desk research was performed for documents related to the TopCare program (Table  6 – details of document analysis in Appendix 1).

The bibliometric analysis of the four domains in the two TopCare non-academic hospitals follows the same methodology as described in Abramo et al. [ 1 ].

We tested the assumption that joint publications are most frequent between a non-academic hospital and its nearest UMC. If the geographical distance between TopCare non-academic hospitals and their collaborative academic partners is described as “nearby”, then they both work within the same region.

The ethnographic interviews were audio-recorded and transcribed in full with the respondents’ permission. These transcripts were subject to close reading and coding by two authors, J.P. and J.O., to identify key themes derived from the theory [ 35 ] (Table  7 in the Appendix). These were then discussed and debated with the wider research team with the goal of developing a critical interpretation of the boundary work done to initiate or enhance research collaboration [cf. 37 ]. The processed interview data were submitted to the respondents for member check. All respondents gave permission to use the data for this study, including the specific quotes. In the Netherlands, this research requires no ethical approval.

Triangulating the results of the document analysis and the interviews enables us to identify different overarching themes within each boundary concept (identity, competence and power). These themes were utilized as a framework for structuring individual paragraphs, which we explain in greater detail in Table  4 in the Results.

Bibliometric analysis of all Dutch UMCs and non-academic hospitals

This section reports the results of the quantitative bibliometric analysis of the output, trends and impact of collaboration between all UMCs and non-academic hospitals from 2009 to 2018/2019. It provides a broad picture of the output – in terms of research publications – of both existing and ongoing collaborations between all UMCs and non-academic hospitals within the specified timeframe. It furthermore describes the analysis results concerning the relationship between collaboration and the geographical distance between two collaborating hospitals.

Output: distribution of the types of collaboration for UMCs and non-academic hospitals from 2009 to 2018/2019

The first step in understanding the degree of collaboration between hospitals is to measure the research output by number of publications. The total number of publications between 2009 and 2018 is shown in Table  8 ( Appendix 1) and Fig.  1 .

Types of collaboration for UMCs and non-academic hospitals from 2009 to 2018/2019. # Total number of publications. Percentage of total (100%) accounted for by single institution, national collaboration and international collaboration

The majority of these publications (89%) are affiliated with UMCs. UMCs, in particular, tend to have a relatively higher proportion of single-institution publications and are more engaged in international collaboration. This pattern may be indicative of UMCs’ enhanced access to research grants and EU subsidies, as well as their active involvement in international consortia.

Collaboration between UMCs and non-academic hospitals appears to be more prevalent and impactful for non-academic hospitals than for UMCs: 70% of all publications originating from a non-academic hospital were the result of joint efforts between a UMC and a non-academic hospital, whereas only 8% of all UMC publications were produced in collaboration with a non-academic hospital (Table  8 in Appendix 1).

Trend analysis of collaboration in relative number of publications

Table  9 Appendix 1) and Fig.  2 show the relative number of publications of all 8 UMCs and all 28 non-academic hospitals in the two periods: 2009–2012/2013 and 2014–2017/2018. For both UMCs and non-academic hospitals, international collaboration accounted for a relatively larger share of publications in recent years.

Type of research collaboration for UMCs and non-academic hospitals over time. Percentage of total (100%) accounted for by single institution, national collaboration and international collaboration in each period

Analysis of relationship between distance and collaboration

As the non-academic hospitals often collaborate with UMCs, it is interesting to analyse these collaborations geographically (distance). The assumption is that geographical proximity matters, with the most-frequent joint publications being between a non-academic hospital and the nearest UMC.

Figure  3 shows that 61% (17 out of 28) of the non-academic hospitals collaborate most frequently with the nearest UMCs. Geographical proximity is thus an important but not the only determining factor in collaboration.

Collaboration with nearest UMC from 2009 to 2018

Impact of collaboration on bibliometric output of UMCs and non-academic hospitals

The mean normalized citation scores (MNCS) shown in Table  2 cover all 8 UMCs and 28 non-academic hospitals.

The MNCS in Table  2 and the mean normalized journal scores (MNJS) in Table  10 (Appendix 1) show similar patterns. The impact score for both UMCs and non-academic hospitals is greatest for international collaboration. Non-academic hospitals’ single-institution publications score lower than the global average, which was defined as 1.

In sum, quantitative analysis exposes two trends. The first is growth in international collaboration for all UMCs and non-academic hospitals over time, also revealing that collaboration leads to higher MNCS impact scores. Second, geographical proximity between UMCs and non-academic hospitals is an important but not the only determining factor in collaboration. This is the context in which the TopCare program operated in 2014–2018.

“TopCare program” case study

This section presents the results of our analysis of the collaboration networks of the two TopCare non-academic hospitals, consisting of: (1) quantitative bibliometric analysis of the output and impact of these networks between 2010 and 2016, along with the geographical distance to their academic partners, and (2) qualitative ethnographic interviews to identify the boundary work conducted by these hospitals.

Bibliometric analysis of the two TopCare non-academic hospitals’ international and national collaboration networks across four domains

The results of the bibliometric analysis indicate the representative positions of the two domains within each TopCare non-academic hospital. Between 2010 and 2016, these hospitals generated a higher number of single-institution publications compared with the average of all non-academic hospitals. Percentage-wise, their output resembled that of the UMCs, underscoring their leading positions in their respective domains. The percentage of publications based on national collaboration in the domains of TopCare hospital #2 is comparable to that of non-academic hospitals overall, while there is more international collaboration in the domains of TopCare hospital #1 than at non-academic hospitals overall (Fig.  4 , Appendix 1 and Fig.  1 ). The impact of the research is above the global average, and the publications have a higher average impact when there is collaboration with international partners; this is true across all four domains (Table  11 in Appendix 1).

In terms of geographical distance, only the neurology domain of TopCare hospital #2 collaborates with an academic partner within the same region. All other domains collaborate with partners outside the region, a striking difference from the geographical results shown in Fig.  3 .

Ethnographic analysis

This section reviews the results of our ethnographic analysis of the two TopCare hospitals from 2014 to 2018. To analyse the boundary work these hospitals performed to initiate and/or enhance productive research collaborations, we use the framework suggested by Santos and Eisenhardt (2005) for examining organizational boundary work through the concepts of identity, competence and power. Table 3 provides a description of each boundary and how these concepts are defined in our case study on the basis of the overarching themes in the document analysis and the interviews.

Identity: enhancing hospitals’ value proposition

In the TopCare program, the non-academic hospitals used their unique history and expertise to create a joint research focus in a domain and to enhance their positions and influence their collaboration with UMCs and universities.

A manager in hospital #1’s lung domain explained the work being done from a historical perspective, emphasizing not only the innovative history of the hospital but also its central position in patient care:

The first-ever lung lavage, lung transplant and angioplasty were performed in this hospital. Nationally, this hospital has always, and we’re talking about 50–60 years ago now, been at the forefront, and has always invested in this line of research and care. So that is truly institutionally built, there is just that history and you can’t just copy that. And we have the numbers: for interstitial lung diseases, we have 2000 patients in our practice and receive 600 new patients per year. (interview with manager at hospital #1 in 2018).

To explain why patient care and research into rare interstitial lung diseases is centred in hospital #1 as a strategic domain focus, a leading international pulmonary physician – a “boundary spanner” (see below) – pointed to the importance of building team expertise and creating facilities:

I lead that care program for interstitial lung diseases and preside over the related research. I’ve often been asked: you’re a professor, so why don’t you go to a UMC, couldn’t you do much more there? But the care was developed here [in this hospital]. The expertise needed to recognize interstitial lung diseases depends not only on me but also on the radiologist and pathologist; together we have a team that can do this. We have created facilities that no other hospital has for these diseases. If I leave to do the same work in a UMC, I’d have to start over and I’d be going back 30 years. (interview with pulmonary physician at hospital #1 in 2014).

The doctors working in this hospital’s lung and heart domains finance the working hours they put into research themselves. “This fits in with the spirit of a top clinical hospital and the entrepreneurial character of our hospital.” (interview with project leader at hospital #1 in 2018).

Hospital #2, the result of a merger in 2016, struggled to find its strategic focus. A surgical oncologist at this hospital clarified one of the disadvantages of the merger: “People are [still] busy dealing with the money and positions, and the gaze is turned inward, the primary processes. So clinical research is very low on the agenda.” She continued by saying that a small project team acting on behalf of the hospital’s board of directors (BoD) was seeking the best-fit profile for the program, which had raised some opposition in departments excluded from the chosen strategic focus. As a consequence, the hospital had begun to showcase its highly specialized care in the field of neurosurgical treatments. It had a long history and was the first to use a Gamma Knife device for treating brain tumours. The experts in this domain could thus act as authorities, and they became a national centre of expertise. Their strategic partner was a nearby UMC, and they treated relevant patients from other hospitals in their region.

To generate impact, research priorities in a domain are aligned with the focus of the hospital. A member of the BoD of hospital #2 stressed the urgency of “specializing or focusing on a particular area of care” and emphasized that the TopCare budget was being utilized to create a joint focus within a domain. The resulting collective identity mobilized internal affairs and was recognized as valuable by third parties. An important reason for joining the TopCare program for both hospitals was to be able to position themselves strategically as attractive and credible research partners:

The focus is on the domains of neurology and trauma because we think as a non-academic hospital we have something extra to offer: the very close relationship between patient care and research, because we have a larger number of patients of this type here than the universities. (interview with care manager at hospital #2 in 2013).

In short, the boundary of identity requires a closer alignment between these hospitals’ research activities and their strategic objectives and organizational mindset, and demands that they also showcase their staff’s expertise. The TopCare program offered opportunities to transform and consolidate their identity by enhancing their value proposition, that is, their unique history, strategic domain focus, expertise and number of patients.

Competence: Enhancing research infrastructures

All domains in the TopCare program chose to utilize the TopCare funding to invest in their research infrastructure, and to build research networks to share and learn. A research infrastructure consists of all the organizational, human, material and technological facilities needed for specialist care and research [ 31 ].

The TopCare data show that funding is essential for generating research impact. A manager at hospital #1 described its current financial circumstances:

A lot of research and much of the care is currently not funded, it is actually paid for mostly by the hospital... We have had massive budgetary adjustments the past two or three years. ...It is increasingly difficult to finance these kinds of activities within your own operation. (interview with manager at hospital #1 in 2018).

The TopCare funding was used to enhance the material infrastructure in hospital #1’s heart domain:

A number of things in healthcare are really terribly expensive, and there is simply no financing at all for them. …Cardiac devices, for example. We are constantly trying things out, but there’s no compensation for it. (interview with project leader at hospital #1 in 2018).

Hospital #1 had a long-standing and firm relationship with a UMC in the lung domain, giving it a solid material infrastructure. For example, there were spaces where researchers, especially PhD students, could meet, collaborate and share knowledge [ 31 ]. Another essential part of the material infrastructure for the lung domain was the biobank, as highlighted by a leading international pulmonary physician:

Our board of directors made funds available through the innovation fund to start up a biobank, but developing it and keeping it afloat has now been made possible thanks to the TopCare funding. It’s a gift from heaven! It will allow for further expansion and we can now seek international cooperation. (interview with pulmonary physician at hospital #1 in 2014).

Notably, the program allowed both non-academic hospitals to digitize their infrastructure, for example, with clinical registration and data management systems. According to an orthopaedic surgeon at hospital #2, “Logistics have been created, which can very easily be applied to other areas. By purchasing a data system, everyone can record data in a similar way.”

Besides investing in data infrastructure, the human dimension was another crucial factor in the research infrastructure. Instead of working on research “at night”, it became embedded in physicians’ working hours. All domains indicated the importance of having researchers, statisticians and data management expertise available to ensure and enhance the quality of research, and both hospitals invested in research staffing.

After losing many research-minded traumatologists to academia, hospital #2 decided to invest in dedicated researchers to form an intermediate layer of full-time senior researchers linked to clinicians within the two domains.

I personally think this is the most important layer in a hospital, with both a doctor and a senior researcher supervising students and PhD candidates. Clinicians ask practical questions and researchers ask a lot of theoretical questions. Both perspectives are needed to change practices. I have also learned that it takes a few years before the two can understand each other’s language. (interview with neurosurgeon at hospital #2 in 2018).

Competence: Finding alignments within hospitals and research networks

The program offered the hospitals opportunities to structure internal forms of collaboration and build a knowledge base within a domain. For example, hospital #1 organized educational sessions with all PhD students in the heart domain.

Having more researchers working in our hospital has given the whole research culture a boost, as well as the fact that they are producing more publications and dissertations. (interview with cardiologist at hospital #1 in 2018).

Hospital #2 also encouraged cross-domain learning by organizing meetings between the neurology and trauma domains.

You know, you may not be able to do much together content-wise, but you can learn a lot from each other in terms of the obstacles you face (interview with project manager at hospital #2 in 2016).

At the beginning there was resistance to participating in the program.

It was doom and gloom; without more support, groups refused to join. That kind of discussion. So the financial details have been important in terms of willingness to participate. (interview with surgical oncologist at hospital #2 in 2018).

Another obstacle was local approval for multicentre studies, which led to considerable delay (interview with psychologist at hospital #2 in 2018). Overall, the TopCare program created a flywheel effect for other domains that proved essential for internal collaborations (interview with surgical oncologist at hospital #2 in 2018).

In hospital #1, collaboration between the heart and lung domains grew closer.

Divisions between the different disciplines are much less pronounced in our hospital than in UMCs. So it’s much easier to work together. We’d already collaborated closely on lung diseases, and this has improved during the program. (interview with cardiologist at hospital #1 in 2016)

At the network level, the TopCare data show that most researchers participated in national networks. For example, the neurology domain in hospital #2 had established a network of 16 non-academic hospitals. Limited funding prevented researchers at non-academic hospitals from attending many international seminars, and they had more trouble building their international networks. One exception concerned the researchers in the lung domain of hospital #1, who expanded their international network by organizing an international seminar during the TopCare program and by contributing to other national and international seminars.

Each TopCare domain provided highly specialized care and wanted to become a centre of expertise. However, a hospital can only provide highly specialized care if research is conducted to determine the best treatment strategies. The data show how the two are interwoven.

For example, a PhD student has sought to collaborate with a UMC on a specific aorta subject in which we have greater expertise and more volume in terms of patients than UMCs. Based on this link with this UMC, a different policy was drawn up and also implemented immediately in all kinds of other UMCs. (interview with cardiologist at hospital #1 in 2018).

Often, a leading scientist who is the driving force behind a domain in a hospital is a “boundary spanner”, a person in a unique position to bridge organizational boundaries and foster research collaboration by “enabling exchange between production and use of knowledge” [ 40 , p. 1176], [ 41 ]. For example, the leading pulmonary physician in hospital #1 is a boundary spanner who has done a huge amount of work to enhance collaboration. With interstitial lung disease care being concentrated here, this professor can offer fellowships and stimulate virtual knowledge-sharing by video conferencing for “second-opinion” consultations. The TopCare funding was used to finance this. The network is successful at a non-academic level.

These consultations are with colleagues in other hospitals and they avoid patients having to be referred. (interview with project leader at hospital #1 in 2018). Our network now [in 2018] consists of more than 14 hospitals, which we call every week to discuss patients with an interstitial lung disease. …UMCs participate indirectly in this network. For example, the north has a specific centre for this disease in a non-academic hospital and a nearby UMC refers patients to this centre, who are then discussed in our network. (interview with pulmonary physician at hospital #1 in 2018).

This physician also noted that the network was still growing; other colleagues from non-academic hospitals wanted to join it.

Yesterday, colleagues from XX and XX were here. And they all said, “I’ve never learned so much about interstitial lung diseases.” We’re imparting enormous amounts of expertise. (interview with pulmonary physician at hospital #1 in 2018).

In sum, focusing on the boundary of competence, the TopCare hospitals created and mobilized resources to invest in their research infrastructure. In every domain, this infrastructure was used to strengthen the relationship between research, care and education, and to build and enhance internal and external research networks to share and learn.

Power: Enhancing the relationship with or finding and mobilizing strategic academic partners

For TopCare non-academic hospitals, the boundary of power is concerned with creating the right sphere of influence, meaning BoDs and administrators attempt to find and mobilize new strategic partners and build mutual relationships with various stakeholders at different levels.

A project leader at hospital #2 emphasized that the additional resources of the TopCare program created an opportunity for the non-academic hospitals “to show our collaborative partners that we’re a valuable partner.” For once, the tables were turned:

We’ve always had a good relationship with one UMC; they always used the data from our surgeries. But it’s nice that we can finally ask them whether they want to join us. That makes it a little more equal, and we can be a clinical partner. (interview with neurosurgeon at hospital #2 in 2018).

One of the requirements in each domain when applying to ZonMw for funding was alignment with academia in a research and innovation network. Collaboration often appeared more difficult at the administrative level when the academic partners worked in the same field of expertise, and tended to be more successful when the partners focused on different fields, where their interests did not conflict. According to a board member at hospital #2 who played a crucial role in a partnership agreement, a conscious decision was taken beforehand to seek partners beyond the medical domain as well.

There may be conflict with other groups within the walls of a UMC and I don’t see that as promising. You have to work together and we aren’t in a real position to do so. (interview with board member at hospital #2 in 2018).

Just before the end of the program, it was announced that this hospital had concluded a partnership agreement with a university to broaden their joint research program alongside neurology and trauma. An important prerequisite was that both organizations invest 1 million euros in the partnership. The board member revealed that the relationship with this university had in fact existed for some time:

So we went and talked to the university and they became interested. Then the top level was reorganized and replaced and we had to start from scratch again. That took a lot of time. Our goals were to awaken the enthusiasm of the board and at least three deans, otherwise it would be a very isolated matter. And we succeeded. Last week we had a matchmaking meeting at the university and there were about 50 pitches showing how we could be of value to each other. (interview with board member at hospital #2 in 2018)

Looking back, he defined the conditions for a successful collaboration with academia:

In terms of substance, the two sides have to be going in the same direction and complement each other, for example, in expertise, techniques, and/or facilities. And what is really important is that people know each other and are willing to meet each other…and there must be appreciation. (interview with board member at hospital #2 in 2018).

The trauma domain in hospital #2 wanted to become a trauma research centre in its region, and after investing in its research infrastructure, it found a new strategic academic partner:

We have also found new partners, for example, the Social Health Care Department of a UMC [name]. And that really has become a strong partnership; the intent was there for years, but we had no money. (interview with epidemiologist at hospital #2 in 2018).

The neurology domain at this hospital worked to form a network with a university of technology and a university social science department.

Officially, our hospital can’t serve as a co-applicant for funding and that is frustrating. However, I am pleased to show that we are contributing to innovation. (interview with neurosurgeon at hospital #2 in 2018).

A board member at this hospital reflected on the qualities needed for research and concluded: “The neuro group has more of those intrinsic qualities than the trauma group. …I think the trauma group is actually at a crossroads and will think twice about whether they can attract capacity to develop the research side or fall back to a very basic level.”

In hospital #1, administrators rejected a proposal to collaborate with the nearest UMC submitted by medical specialists in the heart domain. Past conflicts and unsuccessful ventures still influenced the present, even though the individuals involved had already left.

A further factor was raised by a manager at hospital #1, who reflected on the importance of obtaining a professorship in the heart domain:

If we can, even on the basis of any kind of appointment, obtain a professorship from the heart centre, then yes, that helps! …I think it just helps throughout the whole operation, politically speaking, as extra confirmation, extra legitimization for that status. (interview with manager at hospital #1 in 2016).

Eventually, hospital #1 managed to find alignment with a UMC in another region during the program and a medical specialist from the hospital became a professor by special appointment.

This UMC showed the greatest determination, actually, while we could have chosen to collaborate with the nearest UMC [but we didn’t]. And there was actually also a real click between both the administrators and the specialists. (interview with manager at hospital #1 in 2018).

Additionally, the TopCare data show that, while there may be close alignment with the nearest UMC, collaboration is not limited to this and proximity can sometimes even be detrimental (for example, in some cases hospitals compete for patients). As research and care in the TopCare hospitals’ domains became more specialized, they required the specific expertise of UMCs in other regions.

One critical dependency in the collaboration between a university or UMC and a non-academic hospital is the distribution of dissertation premiums, valued at about €100,000 per successful PhD track. Currently, after completion of a dissertation, the premium goes entirely to the university or UMC, even when much of the candidate’s research and supervision takes place in a non-academic hospital [ 31 ]. This structural difference makes collaboration less financially valuable to non-academic hospitals. For example, the leading pulmonary physician in hospital #1 is a professor who is affiliated with both a UMC and non-academic hospital, a boundary spanner who works across organizational boundaries, is successful in research, and bears responsibility for a significant proportion of the research output in the lung domain and in the collaboration with other organizations. Moreover, he does most of the PhD supervision, and his students do their work in hospital #1. Despite all this work, the dissertation premium goes to the UMC. Although efforts have been made to change this, certain institutional structures are so strongly embedded that it is difficult to open the organizational boundary.

Power: Aligning with the BoDs and administrators of the TopCare non-academic hospitals

During our research, we observed how the BoDs and administrators of the two TopCare hospitals discussed the progress of the program and worked together to learn from each other.

We can learn a lot from hospital #1 regarding the organization of our research, we think. That has been very inspiring. …On the other hand, the focus has been very centred on getting the domain and project requests funded at all. (interview with care manager at hospital #2 in 2013).

The BoDs opted for an approach aimed at building mutual trust and understanding. As a result, their alliance became more intensive during the program. By the time the program’s final report was released, both BoDs were leveraging their power to influence ZonMw’s next step: the follow-up to TopCare. They had a targeted plan for their lobbying. For example, after mutual coordination, the BoD of each hospital sent a letter to the Ministry of Health sketching their vision for the future.

In summary, for the TopCare hospitals, the boundary of power centred on finding alignment with strategic academic partners and the other BoDs and administrators in the TopCare program. Moreover, ties with strategic partners were important for extending the organization’s sphere of influence [ 33 ] in building and enhancing productive research collaborations. These hospitals recognized that they could not dismantle the existing structure of research funding, and they therefore committed themselves to trying to extend the TopCare program. Table 4 summarizes the opportunities and challenges within the three boundary concepts.

In our study, we used a mixed methods research design to explore research collaborations by focusing on the research output and impact of UMCs and non-academic hospitals in the Netherlands and by zeroing in on the boundary work of two Dutch non-academic hospitals for achieving collaboration.

Our bibliometric analysis shows that collaboration matters, especially for non-academic hospitals. Access to research grants, EU funding and international collaborations is harder for non-academic hospitals, and they need to collaborate with UMCs to generate research impact, assessed by means of MNCS impact scores. Conversely, non-academic hospitals are important for UMCs because they have a larger volume of patients. When UMCs and non-academic hospitals collaborate, their impact scores are higher. Impact scores are, moreover, higher for international collaborative publications across all types of hospital and all periods. More in-depth research is needed into why collaboration increases impact.

Bibliometric analysis of the domains of the two TopCare non-academic hospitals underscores their leading role in these domains. Upon receiving TopCare funding, the hospitals had to engage in various forms of boundary work to meet the requirement mandated by ZonMw of establishing a research collaboration with academia. They used the additional program resources to invest [ 33 ] in opening a boundary for research collaboration with academic partners.

Identity work involves creating an image of the organizational unit that legitimizes its research and care status in line with the dominant mindset of the organization. In practice, the relevant unit needs to establish a distinctive history and domain focus that aligns with the organizational strategy of the hospital, in-house expertise and patient flow. This requires coordination work with the BoD. However, not all domains have been successful in creating such an identity. It proved much more difficult for the trauma domain, for example, because their research is not as highly specialized as and more fragmented than the other domains.

Competence work focuses on organizational (a well-functioning science support unit), technological (registration systems) and material (floor space or biobank) infrastructure, depending on individual requirements. Additionally, tremendous efforts go into the human dimension of infrastructure, as TopCare hospitals consider research staff and making time available for doctors to be important conditions for building structurally supportive research programs. In a previous study, we highlighted that collaboration between all non-academic hospitals within the Association of Top Clinical Teaching Hospitals (STZ) is essential for strengthening their research infrastructure [ 42 ], and can also be seen as a matter of efficiency [ 35 ]. Moreover, in each TopCare hospital, competence work served to bring domains together to facilitate shared learning. Knowledge-sharing across departments or communities is an example of opening boundaries to facilitate integration, convergence or enrichment of points of view [ 36 , 43 , 44 ].

Professors with double affiliations can act as boundary spanners. They play a significant role as experts in a domain by creating its distinctive character, and they surmount borders and break down barriers through their network relationships with other hospitals. Additionally, these persons are responsible for a significant share of the research output in their domain and conduct research with worldwide impact in collaboration with other organizations. Their boundary work must be recognized as essential because they bring usable knowledge to the table, create opportunities for improved relationships across disciplines, enhance communication between stakeholders and facilitate more productive research collaborations [cf. 45 ].

The TopCare hospitals do much less work in the power dimension because the domains in which they operate are adjacent to those of academia. Our study shows that more successful, productive research collaborations are created when the hospital’s academic partner works in a complementary but not identical field. Only in one case, the heart domain, did collaboration succeed in an identical field, but that was because the academic partner was located outside of the hospital’s region and was therefore not a competitor. According to Joo et al., a potential partner’s suitability is determined not only by complementarity, their unique contribution to research collaboration in terms of expertise, skills, knowledge, contexts or resources but also by compatibility and capacity. Partner compatibility involves alignment in vision, commitment, trust, culture, values, norms and working styles, which facilitate rapport-building and cross-institutional collaboration [ 46 ]. TopCare data indicate that research collaborations should be managed to ensure all partners can operate as equals [ 47 ]. Partner capacity refers to the ability to provide timely resources (for example, expertise, skills or knowledge) for projects, as well as leadership commitment, community engagement and institutional support for long-term, mission-driven goals, such as the joint research program in neurology and trauma at hospital #2 and a university.

These three qualitative criteria – partner compatibility, complementarity and capacity – are aspects of power dynamics that influence strategic decisions about recruiting research partners. Generally, power dynamics shape a hospital’s strategic choices regarding whether to collaborate, with whom to partner and the extent of the research collaboration [ 48 ]. Future research should examine these power dynamics in a more integrated manner to unlock the full potential of collaboration [ 46 ].

It was possible to unravel how non-academic hospitals participating in the TopCare program engaged in research collaborations with academia. As the program did not interfere with the existing care, research and financing structures within the UMCs, it allowed TopCare non-academic hospitals to also combine top clinical care and research. The boundary concepts allow us to observe a dual dynamic in the collaboration: the opening of boundaries while simultaneously maintaining certain limits. Opening boundaries refers to facilitating collaboration through activities related to identity and competence, while maintaining them involves the power balance. The temporary program did not disrupt the existing power balance associated with the budgetary “academic component” and the dissertation premiums that accrue to academia. Overall, then, the power dimension may well be the primary factor that made it impossible for the TopCare non-academic hospitals to attain their ultimate goal: secure a consistent form of funding for their research and top clinical care. Instead, the national authorities introduced a new, temporary funding program for non-academic hospitals, and preserved the status quo favouring academia.

A key finding is that, if a hospital is successful in establishing coherence between the different forms of boundary work, it can create productive research collaborations and generate research impact. The TopCare hospitals performed boundary work to strengthen their research infrastructure (competence) and their research status (identity) and create a favourable negotiating position opposite academia (power). For example, choosing the lung domain as the hospital’s strategic focus (identity) and establishing a database as a fundamental source of information for research by a boundary spanner (competence) generated sufficient power to make the hospital a key player in this field and a much-respected collaboration partner, nationally and internationally. However, some restrictions remained in place, such as the national lung research network consisting only of non-academic hospitals, with UMCs participating only indirectly.

Another key finding is that possessing a substantial budget is not in itself enough to ensure successful research collaboration. It is clear from this study that extensive boundary work is also needed to facilitate research collaboration. Given the absence of structural funding, the TopCare non-academic hospitals were under pressure to deliver results during the program, making research collaboration even more crucial for them than for the UMCs in this context. Additionally, because highly specialized care and research at the TopCare non-academic hospitals required unique expertise, they had a growing need for collaboration at the national level. Contrary to assumptions and the findings of our analysis of UMCs and non-academic hospitals overall, their collaborative partners were not predominantly located at the nearest UMC.

Does our study align with the literature and support the results of similar initiatives, such as the establishment of Collaborations for Leadership in Applied Health Research and Care (CLAHRC), a regional multi-agency research network of universities and local national health service (NHS) organizations focused on improving patient outcomes in England by conducting and utilizing applied health research [ 49 ]? And what does it contribute to previous research?

While differences exist between the National Health Service (NHS) and the healthcare system in the Netherlands, there are also noteworthy parallels that render a comparison possible. These include encouraging networks to boost research productivity, fostering collaboration within a competitive system and funding research that is relevant to public health priorities. Moreover, building upon the findings of CLAHRC regarding boundary work within a competitive system and developing and funding research that is relevant to patient needs and public health priorities, there are further parallels, such as creating strong local research infrastructures and local networks [ 49 ], and using influential and skilled boundary spanners [ 49 , 50 ]. In addition, we found that research history, strategic domain focus, in-house expertise, patient flows, and network relationships pre-conditioned the TopCare hospitals’ collaboration with academia. Our results further show that, for non-academic hospitals seeking to create productive research collaborations, it is essential to work in complementary fields and to establish a coherence between identity, competence and power.

Our findings indicate that, after opening a boundary with academia, the focus of the TopCare hospitals was on searching for mutual engagement. These hospitals tried to clarify their added value by creating boundaries to distinguish themselves from UMCs, and attempted to extend the TopCare program without it overlapping with the budgetary “academic component”, so that it posed no threat to the UMCs. Boundary-crossing involves a two-way interaction of mutual engagement and commitment to change in practices [ 51 ]. It is likely that the program did not last long enough to instigate changes in practices, as it can take time to develop mutual understanding and foster trusting relationships [ 52 ].

Based on the CLAHRC results and our research findings, the trend towards regionalization in the Netherlands [ 53 ] and a new leading and coordinating role for UMCs in this research landscape [ 52 , 54 ] can only be successful if boundary work is conducted, allowing research-minded non-academic hospitals to:

Build a “collaborative identity” [ 50 , 55 , 56 ] over time with their academic partners (identity);

Establish added value in their research infrastructures compared with that of their academic partners (competence);

Create solid networks for learning and sharing knowledge [ 55 , 57 ] with their academic partners (competence);

Mobilize boundary spanners to bridge disciplinary and professional boundaries in research, teaching and practice [ 49 , 50 , 55 , 58 ] and publish articles in collaboration with academic partners with high research impact (competence);

Find the inspiration and confidence to increase their co-dependence to, for example, gain benefits from interacting with different partners in the field [ 35 ] (power); and

Create long-term collaborations with academia across sectors over time, as well as within sectors; this requires iterative and continual engagement between clinicians, academics, managers, practitioners and patients (power) [ 49 , 52 ].

It is conceivable that the evaluation of the follow-up study to the TopCare program, which will extend to 2025, could unravel these next steps.

Our results demonstrate that collaboration in research is important and should be encouraged. However, the current methods used to assess researchers underestimate this importance. Reward systems and metrics focus on the performance of individual researchers and may even discourage the development of medical research networks and collaboration [ 52 , 59 ]. There is ongoing debate about and rising criticism of the dominance of scientific impact scores as a measure of the performance of health researchers and research organizations [ 60 ]. Other forms of impact, such as the societal impact of medical research, are becoming more important, and different metrics are being developed. Research collaboration among individuals and organizations should be incentivized and rewarded, and should also be embedded in performance assessment and the core competences of all actors involved [ 61 ]. New ways of rewarding research collaboration within organizations should therefore be explored.

Limitations

This study is limited, both geographically and institutionally, to the Netherlands, and factors other than national and international research collaborations may explain the increase in research output and impact. For example, the research articles in our sample have not been analysed on substantive aspects such as methodology and funding. A bias may therefore have been introduced. Furthermore, the research output and impact of the TopCare non-academic hospitals that we measured was limited to the 4-year program period. A further limitation was the use of these hospitals’ research output as a measure of the influence of the TopCare program, as we were interested not only in the short-term effects (publications) but also in the long-term ones (on the work conducted to build research infrastructures). Moreover, the focus in the qualitative material concerning the TopCare program was on the two TopCare non-academic hospitals and, more specifically, on their national rather than their international collaborations.

Research collaboration between non-academic hospitals and academia in the Netherlands pays off in terms of publications and impact. For the publication of scientific articles, collaboration between UMCs and non-academic hospitals appears to be more prevalent and impactful for non-academic hospitals than for UMCs. When UMCs and non-academic hospitals collaborate, their impact scores tend to be higher. More research is needed into why collaboration leads to more impact.

Non-academic hospitals showed a higher rate of collaboration with the nearest UMC, whereas collaborative partners of TopCare hospitals were not predominantly located at the nearest UMC. TopCare hospitals prioritized expertise over geographical proximity as a predicator of their collaborative efforts, particularly as research and care in their domains became more specialized.

Drawing on the additional resources of the TopCare program, participating non-academic hospitals invested significantly in boundary work to open boundaries for research collaboration with academic partners and, simultaneously, to create boundaries that distinguished them from UMCs. Identity work was performed to ensure that their history and domain focuses were coherent with the dominant mindset of their organization, while competence work was done to enhance their research infrastructure. The human dimension of the infrastructure received considerable attention: more research staff, time made available for doctors and recognition that boundary spanners facilitate research collaborations.

Power work to find and mobilize strategic academic partners was mostly focused on complementary fields, as non-academic hospitals work in domains adjacent to those of academia. The TopCare hospitals tended to avoid power conflicts, resulting in a preservation of the status quo favouring academia.

The local research history, strategic domain focus, in-house expertise, patient flows, infrastructure and network relationships of each TopCare hospital influenced collaboration with academia [cf. 37 , 58 . Increased coherence between the different forms of boundary work led to productive research collaborations and generated research impact. To meet future requirements, such as regionalization, further boundary work is needed to create long-term collaborations and new ways of rewarding research collaboration within organizations.

Availability of data and materials

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

The names of the UMCs and non-academic hospitals and their numbers are not up to date due to mergers in the intervening period. The database contains data on eight UMCs; today there are seven, as two UMCs in Amsterdam merged in 2018. There are 28 non-academic hospitals in the database, whereas today 27 such hospitals are members of the Association of Top Clinical Teaching Hospitals ( https://www.stz.nl ). To ensure data consistency, the database remains unchanged.

Abbreviations

Board of directors

Center for Science and Technology Studies

International collaboration

Mean normalized citation score

Mean normalized journal score

National collaboration

Netherlands Federation of University Medical Centers

Single institution

Association of Top Clinical Teaching Hospitals

University medical centre

Abramo G, D’Angelo CA, Di Costa F. Research collaboration and productivity: is there correlation? High Educ. 2009. https://doi.org/10.1007/s10734-008-9139-z .

Article   Google Scholar  

De Solla Price DJ. Little science, big science. New York: Columbia University Press; 1963.

Book   Google Scholar  

Narin F, Carpenter MP. National publication and citation comparisons. JASIS&T. 1975. https://doi.org/10.1002/asi.4630260203 .

Beaver D, Rosen R. Studies in scientific collaboration: part III – professionalization and the natural history of modern scientific co-authorship. Scientometrics. 1979. https://doi.org/10.1007/BF02016308 .

Katz JS, Martin BR. What is research collaboration? Res Policy. 1997. https://doi.org/10.1016/S0048-7333(96)00917-1 .

Clark BY, Llorens JJ. Investments in scientific research: examining the funding threshold effects on scientific collaboration and variation by academic discipline. PSJ. 2012. https://doi.org/10.1111/j.1541-0072.2012.00470.x .

Bozeman B, Fay D, Slade CP. Research collaboration in universities and academic entrepreneurship: the-state-of-the-art. J Technol Transf. 2013. https://doi.org/10.1007/s10961-012-9281-8 .

Van Raan AF. Measuring science. In: Moed HF, Glänzel W, Schmoch U, editors. Handbook of quantitative science and technology research: the use of patent and publication statistics in studies of S&T systems. Dordrecht: Springer; 2004. p. 19–50. https://doi.org/10.1007/1-4020-2755-9_2 .

Chapter   Google Scholar  

Lotka AJ. The frequency distribution of scientific productivity. J Wash Acad Sci. 1926;16:317–23.

Google Scholar  

De Solla Price DJ, Beaver D. Collaboration in an invisible college. Am Psychol. 1966. https://doi.org/10.1037/h0024051 .

Zuckerman H. Nobel laureates in science: patterns of productivity, collaboration, and authorship. Am Sociol Rev. 1967;32:391–403.

Article   CAS   PubMed   Google Scholar  

Morrison PS, Dobbie G, McDonald FJ. Research collaboration among university scientists. High Educ Res Dev. 2003. https://doi.org/10.1080/0729436032000145149 .

Lee S, Bozeman B. The impact of research collaboration on scientific productivity. Soc Stud Sci. 2005. https://doi.org/10.1177/0306312705052359 .

Beaver DB. Collaboration and teamwork in physics. Czechoslov J Phys B. 1986. https://doi.org/10.1007/BF01599717 .

Acedo FJ, Barroso C, Casanueva C, Galán JL. Co-authorship in management and organizational studies: an empirical and network analysis. J Manag Stud. 2006. https://doi.org/10.1111/j.1467-6486.2006.00625.x .

Wuchty S, Jones BF, Uzzi B. The increasing dominance of teams in production of knowledge. Science. 2007. https://doi.org/10.1126/science.1136099 .

Article   PubMed   Google Scholar  

Sooryamoorthy R. Do types of collaboration change citation? Collaboration and citation patterns of South African science publications. Scientometrics. 2009. https://doi.org/10.1007/s11192-009-2126-z .

Gazni A, Didegah F. Investigating different types of research collaboration and citation impact: a case study of Harvard University’s publications. Scientometrics. 2011. https://doi.org/10.1007/s11192-011-0343-8 .

Landry R, Traore N, Godin B. An econometric analysis of the effect of collaboration on academic research productivity. High Educ. 1996. https://doi.org/10.1007/BF00138868 .

Laband DN, Tollison RD. Intellectual collaboration. J Political Econ. 2000. https://doi.org/10.1086/262132 .

Van Raan A. The influence of international collaboration on the impact of research results: some simple mathematical considerations concerning the role of self-citations. Scientometrics. 1998;42(3):423–8.

Glänzel W. National characteristics in international scientific co-authorship relations. Scientometrics. 2001. https://doi.org/10.1023/a:1010512628145 .

Glänzel W, Schubert A. Analysing scientific networks through co-authorship. In: Moed HF, Glänzel W, Schmoch U, editors. Handbook of quantitative science and technology research: the use of patent and publication statistics in studies of S&T systems. Dordrecht: Kluwer; 2004. p. 257–76. https://doi.org/10.1007/1-4020-2755-9_20 .

Didegah F, Thelwall M. Which factors help authors produce the highest impact research? Collaboration, journal and document properties. J Informetr. 2013. https://doi.org/10.1016/J.JOI.2013.08.006 .

Thelwall M, Maflahi N. Academic collaboration rates and citation associations vary substantially between countries and fields. J Assoc Inf Sci Technol. 2020. https://doi.org/10.1002/asi.24315 .

Archibugi D, Coco A. International partnerships for knowledge in business and academia: a comparison between Europe and the USA. Technovation. 2004. https://doi.org/10.1016/S0166-4972(03)00141-X .

Levi M, Sluiter HE, Van Leeuwen T, Rook M, Peeters G. Medisch-wetenschappelijk onderzoek in Nederland: Hoge kwaliteit door samenwerking UMC’s en opleidingsziekenhuizen. NTvG. 2013;157:A6081.

Abramo G, D’Angelo CA, Di Costa F. University-industry research collaboration: a model to assess university capability. High Educ. 2011. https://doi.org/10.48550/arXiv.1811.01763 .

Centraal Bureau voor de Statistiek. Health care institutions; key figures, finance and personnel. https://www.cbs.nl/nl-nl/cijfers/detail/83652ENG . Accessed 6 Mar 2024.

Postma J, Zuiderent-Jerak T. Beyond volume indicators and centralization: toward a broad perspective on policy for improving quality of emergency care. Ann Emerg Med. 2017. https://doi.org/10.1016/j.annemergmed.2017.02.020 .

Postma JP, Van Dongen-Leunis A, Van Hakkaart-van Roijen L, Bal RA. Evaluatie Topzorg. Een evaluatie van 4 jaar specialistische zorg en wetenschappelijk onderzoek in het St. Antonius Ziekenhuis, het Oogziekenhuis en het ETZ. Rotterdam: Erasmus School of Health Policy & Management; 2018.

Gieryn TF. Boundary-work and the demarcation of science from non-science: strains and interests in professional ideologies of scientists. Am Sociol Rev. 1983. https://doi.org/10.2307/2095325 .

Gieryn TF. Cultural boundaries of science: credibility on the line. Chicago: University of Chicago Press; 1999.

Abbott A. The system of professions. Chicago: University of Chicago Press; 1988.

Santos FM, Eisenhardt KM. Organizational boundaries and theories of organization. Organ Sci. 2005. https://doi.org/10.1287/orsc.1050.0152 .

Chreim S, Langley A, Comeau-Vallée M, Huq JL, Reay T. Leadership as boundary work in healthcare teams. Leadership. 2013. https://doi.org/10.1177/174271501246 .

Waring J, Crompton A, Overton C, Roe B. Decentering health research networks: framing collaboration in the context of narrative incompatibility and regional geo-politics. Public Policy Adm. 2022. https://doi.org/10.1177/0952076720911686 .

Siaw CA, Sarpong D. Dynamic exchange capabilities for value co-creation in ecosystems. J Bus Res. 2021. https://doi.org/10.1016/j.jbusres.2021.05.060 .

Velter M, Bitzer V, Bocken N, Kemp R. Boundary work for collaborative sustainable business model innovation: the journey of a Dutch SME. J Bus Models. 2021. https://doi.org/10.5278/jbm.v9i4.6267 .

Bednarek AT, Wyborn C, Cvitanovic C, Meyer R, Colvin RM, Addison PF, et al. Boundary spanning at the science–policy interface: the practitioners’ perspectives. Sustain Sci. 2018. https://doi.org/10.1007/s11625-018-0550-9 .

Article   PubMed   PubMed Central   Google Scholar  

Neal JW, Neal ZP, Brutzman B. Defining brokers, intermediaries, and boundary spanners: a systematic review. Evid Policy. 2022. https://doi.org/10.1332/174426420X16083745764324 .

Van Oijen JCF, Wallenburg I, Bal R, Grit KJ. Institutional work to maintain, repair, and improve the regulatory regime: how actors respond to external challenges in the public supervision of ongoing clinical trials in the Netherlands. PLoS ONE. 2020. https://doi.org/10.1371/journal.pone.0236545 .

Carlile PR. Transferring, translating, and transforming: an integrative framework for managing knowledge across boundaries. Organ Sci. 2004. https://doi.org/10.1287/ORSC.1040.0094 .

Orlikowski WJ. Knowing in practice: enacting a collective capability in distributed organizing. Organ Sci. 2002. https://doi.org/10.1287/orsc.13.3.249.2776 .

Goodrich KA, Sjostrom KD, Vaughan C, Nichols L, Bednarek A, Lemos MC. Who are boundary spanners and how can we support them in making knowledge more actionable in sustainability fields? Curr Opin Environ Sustain. 2020. https://doi.org/10.1016/j.cosust.2020.01.001 .

Joo J, Selingo J, Alamuddin R. Unlocking the power of collaboration. How to develop a successful collaborative network in and around higher education. Ithaka S+R; 2019.

McDonald J, Jayasuriya R, Harris MF. The influence of power dynamics and trust on multidisciplinary collaboration: a qualitative case study of type 2 diabetes mellitus. BMC Health Serv Res. 2012. https://doi.org/10.1186/1472-6963-12-63 .

Harrington S, Fox S, Molinder HT. Power, partnership, and negotiations: the limits of collaboration. WPA-LOGAN. 1998;21:52–64.

Soper B, Hinrichs S, Drabble S, Yaqub O, Marjanovic S, Hanney S, et al. Delivering the aims of the Collaborations for Leadership in Applied Health Research and Care: understanding their strategies and contributions. Health Serv Deliv Res. 2015. https://doi.org/10.3310/hsdr03250 .

Lockett A, El Enany N, Currie G, Oborn E, Barrett M, Racko G, Bishop S, Waring J. A formative evaluation of Collaboration for Leadership in Applied Health Research and Care (CLAHRC): institutional entrepreneurship for service innovation. Health Serv Deliv Res. 2014. https://doi.org/10.3310/hsdr02310 .

Engeström Y. The horizontal dimension of expansive learning: weaving a texture of cognitive trails in the terrain of health care in Helsinki. In: Achtenhagen F, John EG, editors. Milestones of vocational and occupational education and training. Bielefelt: W Bertelmanns Verlag; 2003. p. 152–79.

Gezondheidsraad. Onderzoek waarvan je beter wordt: Een heroriëntatie op umc-onderzoek. Den Haag: Gezondheidsraad; 2016.

van der Woerd O, Schuurmans J, Wallenburg I, van der Scheer W, Bal R. Heading for health policy reform: transforming regions of care from geographical place into governance object. Policy Politics. 2024. https://doi.org/10.1332/03055736Y2024D000000030 .

Iping R, Kroon M, Steegers C, van Leeuwen T. A research intelligence approach to assess the research impact of the Dutch university medical centers. Health Res Policy Syst. 2022. https://doi.org/10.1186/s12961-022-00926-y .

Rycroft-Malone J, Burton C, Wilkinson JE, Harvey G, McCormack B, Baker R, et al. Collective action for knowledge moblisation: a realist evaluation of the collaborations for leadership in applied Health Research and care. Health Serv Deliv Res. 2015. https://doi.org/10.3310/hsdr03440 .

Kislov R, Harvey G, Walshe K. Collaborations for leadership in applied health research and care: lessons from the theory of communities of practice. Implement Sci. 2011. https://doi.org/10.1186/1748-5908-6-64 .

Harvey G, Fitzgerald L, Fielden S, McBride A, Waterman H, Bamford D, et al. The NIHR collaboration for leadership in applied health research and care (CLAHRC) for Greater Manchester: combining empirical, theoretical and experiential evidence to design and evaluate a large-scale implementation strategy. Implement Sci. 2011. https://doi.org/10.1186/1748-5908-6-96 .

Currie G, Lockett A, Enany NE. From what we know to what we do: lessons learned from the translational CLAHRC initiative in England. J Health Serv Res Policy. 2013. https://doi.org/10.1177/1355819613500484 .

Hurley TJ. Collaborative leadership: engaging collective intelligence to achieve results across organisational boundaries. White Paper. Oxford Leadership. 2011.

DORA. The declaration. https://sfdora.org/read . Accessed 6 Mar 2024.

O’Leary R, Gerard C. Collaboration across boundaries: insights and tips from federal senior executives. Washington: IBM Center for The Business of Government; 2012.

Traag VA, Waltman L, Van Eck NJ. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep. 2019;9(1):5223. https://doi.org/10.1038/s41598-019-41695-z .

Article   CAS   Google Scholar  

Download references

Acknowledgements

The authors thank the two reviewers and the members of the Health Care Governance department of Erasmus School of Health Policy & Management, Erasmus University Rotterdam for their helpful comments on earlier drafts. We are particularly indebted to Kor Grit for his helpful comments and critical appraisal of this paper.

The TopCare program was funded by the Netherlands Organization for Health Research and Development (ZonMw) ( www.zonmw.nl/en ) under Grant [Number 80-84200-98-14001]. ZonMw had no role in the design or conduct of the study; the collection, management, analysis and interpretation of the data; or the preparation, review and approval of the manuscript.

Author information

Authors and affiliations.

Erasmus School of Health Policy & Management, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands

Jacqueline C. F. van Oijen, Annemieke van Dongen-Leunis, Jeroen Postma & Roland Bal

Centre for Science and Technology Studies, Leiden University, Leiden, The Netherlands

Thed van Leeuwen

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: J.v.O., A.v.D.L. and T.v.L. (bibliometric analysis of UMCs and non-academic hospitals); A.v.D.L. and T.v.L. (bibliometric analysis of TopCare domains); and J.v.O., J.P. and R.B. (ethnographic interviews in the TopCare program). Formal analysis: J.v.O., A.v.D.L. and T.v.L. (bibliometric analysis of UMCs and non-academic hospitals); A.v.D.L and T.v.L. (bibliometric analysis of TopCare domains); J.v.O., J.B. and R.B. (ethnographic interviews in the TopCare program). Funding acquisition: R.B. (TopCare program). Investigation: A.v.D.L and T.v.L. (database analysis of UMCs and non-academic hospitals and TopCare domains) and J.v.O., J.B. and R.B. (ethnographic interviews in the TopCare program). Methodology: J.v.O., A.v.D.L and T.v.L. (bibliometric analysis of UMCs and non-academic hospitals); A.v.D.L and T.v.L. (bibliometric analysis of TopCare domains); and J.v.O., J.B. and R.B. (ethnographic interviews in the TopCare program). Project administration: T.v.L. and A.v.D.L (bibliometric analysis of UMCs and non-academic hospitals and TopCare domains) and J.P. (TopCare program). Supervision: T.v.L. (bibliometric analysis of UMCs and non-academic hospitals and TopCare domains) and R.B. (bibliometric analysis of UMCs and non-academic hospitals and TopCare domains, and ethnographic interviews in the TopCare program). Visualization: A.v.D.L and T.v.L. (bibliometric analysis of UMCs and non-academic hospitals and TopCare domains). Original draft: J.v.O., A.v.D.L and R.B. Draft & revision: J.v.O., A.v.D.L, J.P., T.v.L. and R.B. All authors read and approved the final manuscript (and agreed to be both personally accountable for their own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, would be appropriately investigated and resolved and that the resolution would be documented in the literature).

Corresponding author

Correspondence to Jacqueline C. F. van Oijen .

Ethics declarations

Ethics approval and consent to participate.

Not applicable; at the time we were conducting the research, ethical approval was not required. Nowadays our facility has an Ethics Committee that assesses research proposals involving human subjects (including interview studies), but this was not the case then. This study is not subject to the Dutch Medical Research Involving Human Subjects Act (WMO); it concerns collaboration on medical research in TopCare non-academic hospitals. For research not subject to the WMO, local policy and applicable procedures apply; as the TopCare program began in 2014, there were, as yet, no institutional rules in this area.

Consent for publication

Member check is part of our policy of informed consent of respondents and consent for publication. Specifically, we gave respondents the opportunity to peruse and add to quotes from their semi-structured interviews and to confirm our interpretation. The focus was on confirming and amending the quote and verifying the interpretation. The research team discussed the feedback received from the respondents and weighed it against the context of data analysis. Any disagreement on a respondent’s feedback was discussed directly with the respondent until consensus was reached. The STZ and NFU have given permission to use the data collected by CWTS on behalf of the NFU and STZ for the bibliometric analysis of this study. They have taken note of the results of this study and agreed to its publication.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

See Fig.  4 and Tables 5 , 6 , 7 , 8 , 9 , 10 and 11 .

UMCs produce 18 times (= 27,592/1503) more SI, four times (= 42,557/10880) more NC and 14 times (82,540/5896) more IC publications than non-academic hospitals.

Of all publications, 89% (= 152,688/170967) are attributed to UMCs and 11% (18,279/170967) to non-academic hospitals.

Joint publications in national collaboration: 82% (= 8943/10880) non-academic hospitals and 21% (= 8943/42557) UMCs.

Joint international publications: 66% (= 3874/5896) non-academic hospitals and 5% (= 3874/82540) UMCs.

Joint publications: 70% (= 12,816/18279) non-academic hospitals and 8% (= 12,816/152688) UMCs.

Relationship between joint publications and total publications in each type of collaboration: 17% (= 8943/53436) national collaboration and 4% (= 3874/88435) international collaboration.

Types of collaboration involving TopCare hospitals #1 and #2 between 2010 and 2016. #, total number of publications

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

van Oijen, J.C.F., van Dongen-Leunis, A., Postma, J. et al. Achieving research impact in medical research through collaboration across organizational boundaries: Insights from a mixed methods study in the Netherlands. Health Res Policy Sys 22 , 72 (2024). https://doi.org/10.1186/s12961-024-01157-z

Download citation

Received : 31 December 2022

Accepted : 31 May 2024

Published : 25 June 2024

DOI : https://doi.org/10.1186/s12961-024-01157-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Collaboration
• Research impact
• Bibliometric analysis
• Organizational boundary work

Health Research Policy and Systems

ISSN: 1478-4505

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

• Open access
• Published: 28 June 2024

Perceived efficacy of case analysis as an assessment method for clinical competencies in nursing education: a mixed methods study

• Basma Mohammed Al Yazeedi   ORCID: orcid.org/0000-0003-2327-6918 1 ,
• Lina Mohamed Wali Shakman 1 ,
• Sheeba Elizabeth John Sunderraj   ORCID: orcid.org/0000-0002-9171-7239 1 ,
• Harshita Prabhakaran   ORCID: orcid.org/0000-0002-5470-7066 1 ,
• Judie Arulappan 1 ,
• Erna Judith Roach   ORCID: orcid.org/0000-0002-5817-8886 1 ,
• Aysha Al Hashmi 1 , 2 &
• Zeinab Al Azri   ORCID: orcid.org/0000-0002-3376-9380 1  

BMC Nursing volume  23 , Article number:  441 ( 2024 ) Cite this article

Metrics details

Case analysis is a dynamic and interactive teaching and learning strategy that improves critical thinking and problem-solving skills. However, there is limited evidence about its efficacy as an assessment strategy in nursing education.

This study aimed to explore nursing students’ perceived efficacy of case analysis as an assessment method for clinical competencies in nursing education.

This study used a mixed methods design. Students filled out a 13-item study-advised questionnaire, and qualitative data from the four focus groups was collected. The setting of the study was the College of Nursing at Sultan Qaboos University, Oman. Descriptive and independent t-test analysis was used for the quantitative data, and the framework analysis method was used for the qualitative data.

The descriptive analysis of 67 participants showed that the mean value of the perceived efficacy of case analysis as an assessment method was 3.20 (SD = 0.53), demonstrating an 80% agreement rate. Further analysis indicated that 78.5% of the students concurred with the acceptability of case analysis as an assessment method (mean = 3.14, SD = 0.58), and 80.3% assented its association with clinical competencies as reflected by knowledge and cognitive skills (m = 3.21, SD = 0.60). No significant difference in the perceived efficacy between students with lower and higher GPAs (t [61] = 0.05, p  > 0.05) was identified Three qualitative findings were discerned: case analysis is a preferred assessment method for students when compared to MCQs, case analysis assesses students’ knowledge, and case analysis assesses students’ cognitive skills.

Conclusions

This study adds a potential for the case analysis to be acceptable and relevant to the clinical competencies when used as an assessment method. Future research is needed to validate the effectiveness of case analysis exams in other nursing clinical courses and examine their effects on academic and clinical performance.

Peer Review reports

Introduction

Nurses play a critical role in preserving human health by upholding core competencies [ 1 ]. Clinical competence in nursing involves a constant process of acquiring knowledge, values, attitudes, and abilities to deliver safe and high-quality care [ 2 , 3 ]. Nurses possessing such competencies can analyze and judge complicated problems, including those involving crucial patient care, ethical decision-making, and nurse-patient disputes, meeting the constantly altering health needs [ 4 , 5 ]. To optimize the readiness of the new graduates for the challenging clinical work environment needs, nurse leaders call for integrating clinical competencies into the nursing curriculum [ 6 , 7 ] In 2021, the American Association of Colleges of Nursing (AACN) released updated core competencies for professional nursing education [ 8 ]. These competencies were classified into ten fundamental essentials, including knowledge of nursing practice and person-centered care (e.g. integrate assessment skills in practice, diagnose actual or potential health problems and needs, develop a plan of care), representing clinical core competencies.

Nursing programs emphasize clinical competencies through innovative and effective teaching strategies, including case-based teaching (CBT) [ 9 ]. CBT is a dynamic teaching method that enhances the focus on learning goals and increases the chances of the instructor and students actively participating in teaching and learning [ 10 , 11 ]. Additionally, it improves the students’ critical thinking and problem-solving skills and enriches their capacity for independent study, cooperation capacity, and communication skills [ 12 , 13 , 14 , 15 ]. It also broadens students’ perspectives and helps develop greater creativity in fusing theory and practice [ 16 , 17 , 18 , 19 , 20 ]. As the learning environment significantly impacts the students’ satisfaction, case analysis fosters a supportive learning atmosphere and encourages active participation in learning, ultimately improving their satisfaction [ 21 , 22 ].

In addition to proper teaching strategies for clinical competencies, programs are anticipated to evaluate the students’ attainment of such competencies through effective evaluation strategies [ 23 ]. However, deploying objective assessment methods for the competencies remains challenging for most educators [ 24 ]. The standard assessment methods used in clinical nursing courses, for instance, include clinical evaluations (direct observation), skills checklists, Objective Structured Clinical Examination (OSCE), and multiple-choice questions (MCQs) written exams [ 25 ]. MCQs tend to test the recall of factual information rather than the application of knowledge and cognitive skills, potentially leading to assessment inaccuracies [ 26 ].

Given the aforementioned outcomes of CBT, the deployment of case analysis as a clinical written exam is more closely aligned with the course’s expected competencies. A mixed methods study was conducted among forty nursing students at the University of Southern Taiwan study concluded that the unfolding case studies create a safe setting where nursing students can learn and apply their knowledge to safe patient care [ 6 ]. In a case analysis, the patient’s sickness emerges in stages including the signs and symptoms of the disease, urgent care to stabilize the patient, and bedside care to enhance recovery. Thus, unfolding the case with several scenarios helps educators track students’ attained competencies [ 27 ]. However, case analysis as an assessment method is sparsely researched [ 28 ]. A literature review over the past five years yielded no studies investigating case analysis as an assessment method, necessitating new evidence. There remains uncertainty regarding its efficacy as an assessment method, particularly from the students’ perspectives [ 29 ]. In this study, we explored the undergraduate nursing students’ perceived efficacy of case analysis as an assessment method for clinical competencies. Results from this study will elucidate the position of case analysis as an assessment method in nursing education. The potential benefits are improved standardization of clinical assessment and the ability to efficiently evaluate a broad range of competencies.

Research design

Mixed-method research with a convergent parallel design was adopted in the study. This approach intends to converge two data types (quantitative and qualitative) at the interpretation stage to ensure an inclusive research problem analysis [ 30 ]. The quantitative aspect of the study was implemented through a cross-sectional survey. The survey captured the perceived efficacy of using case analysis as an assessment method in clinical nursing education. The qualitative part of the study was carried out through a descriptive qualitative method using focus groups to provide an in-depth understanding of the perceived strengths experienced by the students.

Study setting

Data were collected in the College of Nursing at Sultan Qaboos University (SQU), Oman, during the Spring and Fall semesters of 2023. At the end of each clinical course, the students have a clinical written exam and a clinical practical exam, which constitute their final exam. Most clinical courses use multiple-choice questions (MCQs) in their written exam. However, the child health clinical course team initiated the case analysis as an assessment method in the clinical written exam, replacing the MCQs format.

Participants

For this study, the investigators invited undergraduate students enrolled in the child health nursing clinical course in the Spring and Fall semesters of 2023. Currently, the only course that uses case analysis is child health. Other courses use MCQs. A total enumeration sampling technique was adopted. All the students enrolled in child health nursing clinical courses in the Spring and Fall 2023 semesters were invited to participate in the study. In the Spring, 36 students registered for the course, while 55 students were enrolled in the Fall. We included students who completed the case analysis as a final clinical written exam on the scheduled exam time. Students who did not show up for the exam during the scheduled time and students not enrolled in the course during the Spring and Fall of 2023 were excluded. Although different cases were used each semester, both had the same structure and level of complexity. Further, both cases were peer-reviewed.

Case analysis format

The format presents open-ended questions related to a clinical case scenario. It comprises three main sections: Knowledge, Emergency Room, and Ward. The questions in the sections varied in difficulty based on Bloom’s cognitive taxonomy levels, as presented in Table  1 . An answer key was generated to ensure consistency among course team members when correcting the exam. Three experts in child health nursing peer-reviewed both the case analysis exam paper and the answer key paper. The students were allocated two hours to complete the exam.

Study instruments

Quantitative stage.

The researchers developed a study questionnaire to meet the study objectives. It included two parts. The first was about the demographic data, including age, gender, type of residence, year in the program, and cumulative grade point average (GPA). The second part comprised a 13-item questionnaire assessing the perceived efficacy of case analysis as an assessment method. The perceived efficacy was represented by the acceptability of case analysis as an assessment method (Items 1–5 and 13) and the association with clinical competencies (Items 6 to 12). Acceptability involved format organization and clarity, time adequacy, alignment with course objectives, appropriateness to students’ level, and recommendation for implementation in other clinical nursing courses. Clinical competencies-related items were relevant to knowledge (motivation to prepare well for the exam, active learning, interest in topics, collaboration while studying) and cognitive skills (critical thinking, decision-making, and problem-solving skills) (The questionnaire is attached as a supplementary document).

The questionnaire is answered on a 4-point Likert scale: 1 = strongly disagree, 2 = disagree, 3 = agree, 4 = strongly agree. Higher scores indicated better perceived efficacy and vice versa. The tool underwent content validity testing with five experts in nursing clinical education, resulting in an item-content validity index ranging from 0.7 to 1. The Cronbach alpha was 0.83 for acceptability and 0.90 for clinical competencies.

Qualitative stage

For the focus group interviews, the investigators created a semi-structured interview guide to obtain an in-depth understanding of the students’ perceived strengths of case analysis as an assessment method. See Table  2 .

Data collection

Data was collected from the students after they gave their written informed consent. Students were invited to fill out the study questionnaire after they completed the case analysis as a clinical written exam.

All students in the child health course were invited to participate in focus group discussions. Students who approached the PI to participate in the focus group discussion were offered to participate in four different time slots. So, the students chose their time preferences. Four focus groups were conducted in private rooms at the College of Nursing. Two trained and bilingual interviewers attended the focus groups, one as a moderator while the other took notes on the group dynamics and non-verbal communication. The discussion duration ranged between 30 and 60 min. After each discussion, the moderator transcribed the audio recording. The transcriptions were rechecked against the audio recording for accuracy. Later, the transcriptions were translated into English by bilingual researchers fluent in Arabic and English for the analysis.

Rigor and trustworthiness

The rigor and trustworthiness of the qualitative method were enhanced using multiple techniques. Firstly, quantitative data, literature reviews, and focus groups were triangulated. Participants validated the summary after each discussion using member checking to ensure the moderator’s understanding was accurate. Third, the principal investigator (PI) reflected on her assumptions, experiences, expectations, and feelings weekly. In addition, the PI maintained a detailed audit trail of study details and progress. The nursing faculty conducted the study with experience in qualitative research and nursing education. This report was prepared following the Standard for Reporting Qualitative Research (SRQR) protocol [ 31 ].

Data analysis

Quantitative data were entered in SPSS version 24 and analyzed using simple descriptive analysis using means, standard deviations, and percentages. After computing the means of each questionnaire item, an average of the means was calculated to identify the perceived efficacy rate. A similar technique was used to calculate the rate of acceptability and clinical competencies. The percentage was calculated based on the mean: gained score/total score* 100. In addition, the investigators carried out an independent t-test to determine the relationship between the perceived efficacy and students’ GPA.

The qualitative data were analyzed using the framework analysis method. In our analysis, we followed the seven interconnected stages of framework analysis: (1) transcription, (2) familiarization with the interview, (3) coding, (4) developing a working analytical framework, (5) applying the analytical framework, (6) charting data into framework matrix and (7) interpreting the data [ 32 ]. Two members of the team separately analyzed the transcriptions. Then, they discussed the coding, and discrepancies were solved with discussion.

Mixed method integration

In our study, the quantitative and qualitative data were analyzed separately, and integration occurred at the interpretation level by merging the data [ 33 ]. As a measure of integration between qualitative and quantitative data, findings were assessed through confirmation, expansion, and discordance. If both data sets confirmed each other’s findings, it was considered confirmation, and if they expanded each other’s insight, it was considered expansion. Discordance was determined if the findings were contradictory.

Ethical considerations

Ethical approval was obtained from the Research and Ethics Committee of the College of Nursing, SQU (CON/NF/2023/18). Informed consent was collected, and no identifiable information was reported. For the focus group interviews, students were reassured that their grades were finalized, and their participation would not affect their grades. Also, the interviewers were instructed to maintain a non-judgmental and non-biased position during the interview. Data were saved in a locked cabinet inside a locked office room. The electronic data were saved in a password-protected computer.

The results section will present findings from the study’s quantitative and qualitative components. The integration of the two data types is described after each qualitative finding.

Quantitative findings

We analyzed the data of 67 participants, representing a 73.6% response rate. The mean age was 21.0 years old (SD 0.73) and 36.4% were male students. See Table  3 for more details.

The descriptive analysis showed that the mean value of the perceived efficacy of case analysis as an assessment method was 3.20 (SD = 0.53), demonstrating an 80% agreement rate. Further analysis indicated that 78.5% of the students concurred the acceptability of case analysis as an assessment method (mean = 3.14, SD = 0.58) and 80.3% (m = 3.21, SD = 0.60) assented the clinical competencies associated with it.

For the items representing acceptability, 81.8% of the students agreed that the case analysis was written clearly, and 80.3% reported that it was well organized. As per the questions, 81% described they were appropriate to their level, and 79.8% agreed upon their alignment with the course objectives. Moreover, the time allocated was adequate for 74.5% of the students, and 73.5% recommend using case analysis as an evaluation strategy for other clinical written examinations.

Regarding the clinical competencies, 77.3% of students agreed that the case analysis motivated them to prepare well for the exam, 81.3% reported that it encouraged them to be active in learning, and 81.0% indicated that it stimulated their interest in the topics discussed in the course. Additionally, 76.5% of the students agreed that the case analysis encouraged them to collaborate with other students when studying for the exam. Among the students, 82.5% reported that the case analysis as an assessment method enhanced their critical thinking skills, 81.0% agreed that it helped them practice decision-making skills, and 81.8% indicated that it improved their problem-solving abilities. See Table  4 .

The independent t-test analysis revealed no significant difference in the perceived efficacy between students with lower and higher GPAs (t [61] = 0.05, p  > 0.05). Further analysis showed that the means of acceptability and clinical competencies were not significantly different between the lower GPA group and higher GPA group, t [62] = 0.72, p  > 0.05 and t [63] = -0.83, p  > 0.05, respectively (Table  5 ).

Qualitative findings

A total of 22 had participated in four focus groups, each group had 5–6 students. The qualitative framework analysis revealed three main findings; case analysis is a preferred assessment method to students when compared to MCQs, case analysis assesses students’ knowledge, and case analysis assesses students’ cognitive skills.

Qualitative Finding 1: case analysis is a preferred assessment method to students when compared to MCQs

Most of the students’ statements about the case analysis as an assessment method were positive. One student stated, “Previously, we have MCQs in clinical exams, but they look as if they are theory exams. This exam makes me deal with cases like a patient, which is good for clinical courses.” . At the same time, many students conveyed optimism about obtaining better grades with this exam format. A student stated, “Our grades, with case analysis format, will be better, … may be because we can write more in open-ended questions, so we can get some marks, in contrast to MCQs where we may get it right or wrong” . On the other hand, a few students suggested adding multiple-choice questions, deleting the emergency department section, and lessening the number of care plans in the ward section to secure better grades.

Although the case analysis was generally acceptable to students, they have repeatedly expressed a need to allocate more time for this type of exam. A student stated, “The limited time with the type of questions was a problem, …” . When further discussion was prompted to understand this challenge, we figured that students are not used to handwriting, which has caused them to be exhausted during the exam. An example is “writing is time-consuming and energy consuming in contrast to MCQs …” . These statements elucidate that the students don’t necessarily mind writing but recommend more practice as one student stated, “More experience of this type of examination is required, more examples during clinical practice are needed.” Some even recommended adopting this format with other clinical course exams by saying “It’s better to start this method from the first year for the new cohort and to apply it in all other courses.”

Mixed Methods Inference 1: Confirmation and Expansion

The abovementioned qualitative impression supports the high acceptability rate in quantitative analysis. In fact, there is a general agreement that the case analysis format surpasses the MCQs when it comes to the proper evaluation strategies for clinical courses. Expressions in the qualitative data revealed more details, such as the limited opportunities to practice handwriting, which negatively impacted the perceived adequacy of exam time.

Qualitative Finding 2: case analysis assesses students’ knowledge

Students conferred that they were reading more about the disease pathophysiology, lab values, and nursing care plans, which they did not usually do with traditional means of examination. Examples of statements include “… before we were not paying attention to the normal lab results but …in this exam, we went back and studied them which was good for our knowledge” and “we cared about the care plan. In previous exams, we were not bothered by these care plans”. Regarding the burden that could be perceived with this type of preparation, the students expressed that this has helped them prepare for the theory course exam; as one student said, “We also focus on theory lectures to prepare for this exam …. this was very helpful to prepare us for the theory final exam as well.” However, others have highlighted the risks of limiting the exam’s content to one case analysis. The argument was that some students may have not studied the case completely or been adequately exposed to the case in the clinical setting. To solve this risk, the students themselves advocated for frequent case group discussions in the clinical setting as stated by one student: “There could be some differences in the cases that we see during our clinical posting, for that I recommend that instructors allocate some time to gather all the students and discuss different cases.” Also, the participants advocated for more paper-based case analysis exercises as it is helpful to prepare them for the exams and enhance their knowledge and skills.

Mixed Methods Inferences 2: Confirmation and Expansion

The qualitative finding supports the quantitative data relevant to items 6, 7, and 8. Students’ expressions revealed more insights, including the acquisition of deeper knowledge, practicing concept mapping, and readiness for other course-related exams. At the same time, students recommended that faculty ensure all students’ exposure to common cases in the clinical setting for fair exam preparation.

Qualitative Finding 3. case analysis assesses students’ cognitive skills

Several statements conveyed how the case analysis format helped the students use their critical thinking and analysis skills. One student stated, “It, the case analysis format, enhanced our critical thinking skills as there is a case with given data and we analyze the case….” . Therefore, the case analysis format as an exam is potentially a valid means to assess the student’s critical thinking skills. Students also conveyed that the case analysis format helped them link theory to practice and provided them with the platform to think like real nurses and be professional. Examples of statements are: “…we connect our knowledge gained from theory with the clinical experience to get the answers…” and “The questions were about managing a case, which is what actual nurses are doing daily.” Another interesting cognitive benefit to case analysis described by the students was holistic thinking. For example, one student said, “Case analysis format helped us to see the case as a whole and not only from one perspective.”

Mixed Methods Inferences 3: Confirmation

The quantitative data indicated mutual agreement among the students that the case analysis enhanced their critical thinking, decision-making, and problem-solving skills. The students’ statements from the interviews, including critical thinking, linking theory to practice, and holistic thinking, further supported these presumptions.

This research presents the findings from a mixed methods study that explored undergraduate nursing students’ perceived efficacy of using case analysis as an assessment method. The perceived efficacy was reflected through acceptability and association with two core competencies: knowledge and cognitive skills. The study findings showed a high rate of perceived efficacy of case analysis as an assessment method among nursing students. Additionally, three findings were extracted from the qualitative data that further confirmed the perceived efficacy: (1) case analysis is a preferred assessment method to students compared to MCQs, (2) case analysis assesses students’ knowledge, and (3) case analysis assesses students’ cognitive skills. Moreover, the qualitative findings revealed details that expanded the understanding of the perceived efficacy among nursing students.

Previous literature reported students’ preference for case analysis as a teaching method. A randomized controlled study investigated student’s satisfaction levels with case-based teaching, in addition to comparing certain outcomes between a traditional teaching group and a case-based teaching group. They reported that most students favored the use of case-based teaching, whom at the same time had significantly better OSCE scores compared to the other group [ 34 ]. As noted, this favorable teaching method ultimately resulted in better learning outcomes and academic performance. Although it may be challenging since no answer options are provided, students appreciate the use of case analysis format in their exams because it aligns better with the course objectives and expected clinical competencies. The reason behind students’ preference for case analysis is that it allows them to interact with the teaching content and visualize the problem, leading to a better understanding. When case analysis is used as an assessment method, students can connect the case scenario presented in the exam to their clinical training, making it more relevant.

In this study, students recognized the incorporation of nursing knowledge in the case analysis exam. They also acknowledged improved knowledge and learning abilities similar to those observed in case-based teaching. Boney et al. (2015) reported that students perceived increased learning gains and a better ability to identify links between different concepts and other aspects of life through case-based teaching [ 35 ]. Additionally, case analysis as an exam promotes students’ in-depth acquirement of knowledge through the type of preparation it entails. Literature suggested that case-based teaching promotes self-directed learning with high autonomous learning ability [ 34 , 36 ]. Thus, better achievement in the case analysis exam could be linked with a higher level of knowledge, making it a suitable assessment method for knowledge integration in nursing care.

The findings of this study suggest that case analysis can be a useful tool for evaluating students’ cognitive skills, such as critical thinking, decision-making, and problem-solving. A randomized controlled study implied better problem-solving abilities among the students in the case-based learning group compared to those in the traditional teaching methods group [ 12 ]. Moreover, students in our study conveyed that case analysis as an exam was an opportunity for them to think like real nurses. Similar to our findings, a qualitative study on undergraduate nutrition students found that case-based learning helped students develop professional competencies for their future practice, in addition to higher-level cognitive skills [ 37 ]. Therefore, testing students through case analysis allows educators to assess the student’s readiness for entry-level professional competencies, including the thinking process. Also, to evaluate students’ high-level cognitive skills according to Bloom’s taxonomy (analysis, synthesis, and evaluation), which educators often find challenging.

Case analysis as an assessment method for clinical courses is partially integrated in case presentation or OSCE evaluation methods. However, the written format is considered to be more beneficial for both assessment and learning processes. A qualitative study was conducted to examine the impact of paper-based case learning versus video-based case learning on clinical decision-making skills among midwifery students. The study revealed that students paid more attention and were able to focus better on the details when the case was presented in a paper format [ 38 ]. Concurrently, the students in our study recommended more paper-based exercises, which they believed would improve their academic performance.

This study has possible limitations. The sample size was small due to the limited experience of case analysis as a clinical written exam in the program. Future studies with larger sample sizes and diverse nursing courses are needed for better generalizability.

Implications

Little evidence relates to the efficacy of case analysis as an evaluation method, suggesting the novelty of this study. Despite the scarcity of case-based assessment studies, a reader can speculate from this study’s findings that there is a potential efficacy of case analysis as an assessment method in nursing education. Future research is warranted to validate the effectiveness of case-analysis assessment methods and investigate the effects of case-analysis exams on academic and clinical performance.

Overall, our findings are in accordance with the evidence suggesting students’ perceived efficacy of case analysis as a teaching method. This study adds a potential for the case analysis to be acceptable and relevant to the clinical competencies when used as an assessment method. Future research is needed to validate the effectiveness of case analysis exams in other nursing clinical courses and examine their effects on academic and clinical performance.

Data availability

The datasets used and/or analyzed during the current study are available fromthe Principal Investigator (BAY) upon reasonable request.

Iriarte-Roteta A, Lopez‐Dicastillo O, Mujika A, Ruiz‐Zaldibar C, Hernantes N, Bermejo‐Martins E, Pumar‐Méndez MJ. Nurses’ role in health promotion and prevention: a critical interpretive synthesis. J Clin Nurs. 2020;29(21–22):3937–49. https://doi.org/10.1111/jocn.15441

Article   PubMed   Google Scholar  

Fukada M. Nursing competency: definition, structure and development. Yonago Acta Med. 2018;61(1):001–7. https://doi.org/10.33160/yam.2018.03.001

Article   Google Scholar  

Nabizadeh-Gharghozar Z, Alavi NM, Ajorpaz NM. Clinical competence in nursing: a hybrid concept analysis. Nurse Educ Today. 2021;97:104728. https://doi.org/10.1016/j.nedt.2020.104728

Allande-Cussó R, Fernández-García E, Porcel-Gálvez AM. Defining and characterising the nurse–patient relationship: a concept analysis. Nurs Ethics. 2021;29(2):462–84. https://doi.org/10.1177/09697330211046651

Butts JB, Rich KL. Nursing ethics: across the curriculum and into practice. Jones & Bartlett Learning; 2019.

Chen W, Shah UV, Brechtelsbauer C. A framework for hands-on learning in chemical engineering education—training students with the end goal in mind. Educ Chem Eng. 2019;28:25–9.

Willman A, Bjuresäter K, Nilsson J. Newly graduated registered nurses’ self-assessed clinical competence and their need for further training. Nurs Open. 2020;7(3):720–30. https://doi.org/10.1002/nop2.443

Article   PubMed   PubMed Central   Google Scholar  

American Association of Colleges of Nursing. (2021). The essentials: Core competencies for professional nursing education. In. Retrieved from n.d.). American Association of Colleges of Nursing. https://www.aacnnursing.org/Portals/0/PDFs/Publications/Essentials-2021.pdf

Kaur G, Rehncy J, Kahal KS, Singh J, Sharma V, Matreja PS, Grewal H. Case-based learning as an effective tool in teaching pharmacology to undergraduate medical students in a large group setting. J Med Educ Curric Dev. 2020;7:2382120520920640.

Patiraki E, Katsaragakis S, Dreliozi A, Prezerakos P. Nursing care plans based on NANDA, nursing interventions classification, and nursing outcomes classification: the investigation of the effectiveness of an educational intervention in Greece. Int J Nurs Knowl. 2017;28:88–93.

Cui C, Li Y, Geng D, Zhang H, Jin C. The effectiveness of evidence-based nursing on development of nursing students ‘critical thinking: a meta-analysis. Nurse Educ Today. 2018;65:46–53.

Bi M, Zhao Z, Yang J, Wang Y. Comparison of case-based learning and traditional method in teaching postgraduate students of medical oncology. Med Teach. 2019;41(10):1124–8.

Seshan V, Matua GA, Raghavan D, Arulappan J, Al Hashmi I, Roach EJ, Prince EJ. Case study analysis as an effective teaching strategy: perceptions of undergraduate nursing students from a Middle Eastern Country. SAGE Open Nurs. 2021;7:23779608211059265.

PubMed   PubMed Central   Google Scholar  

Slieman TA, Camarata T. Case-based group learning using concept maps to achieve multiple educational objectives and behavioral outcomes. J Med Educ Curric Dev. 2019;6:2382120519872510.

Yu Z, Hu R, Ling S, Zhuang J, Chen Y, Chen M, Lin Y. Effects of blended versus offline case-centered learning on the academic performance and critical thinking ability of undergraduate nursing students: a cluster randomized controlled trial. Nurse Educ Pract. 2021;53:103080.

Chan AW, Chair SY, Sit JW, Wong EM, Lee DT, Fung OW. Case-based web learning versus face-to-face learning: a mixed-method study on university nursing students. J Nurs Res. 2016;24(1):31–40.

Hong S, Yu P. Comparison of the effectiveness of two styles of case-based learning implemented in lectures for developing nursing students’ critical thinking ability: a randomized controlled trial. Int J Nurs Stud. 2017;68:16–24.

Shohani M, Bastami M, Gheshlaghi LA, Nasrollahi A. Nursing student’s satisfaction with two methods of CBL and lecture-based learning. BMC Med Educ. 2023;23(1):1–5.

Tan KW. Using Teaching Cases for Achieving Bloom’s High-Order Cognitive Levels: An Application in Technically-Oriented Information Systems Course (2017). 2017 Proceedings. 1. http://aisel.aisnet.org/siged2017/1

Farashahi M, Tajeddin M. Effectiveness of teaching methods in business education: a comparison study on the learning outcomes of lectures, case studies and simulations. Int J Manage Educ. 2018;16(1):131–42.

Google Scholar  

Farha RJA, Zein MH, Al Kawas S. Introducing integrated case-based learning to clinical nutrition training and evaluating students’ learning performance. J Taibah Univ Med Sci. 2021;16(4):558–64.

Picciano AG. Theories and frameworks for Online Education: seeking an Integrated Model. Online Learn. 2017;213:166–90.

Bezanilla MJ, Fernández-Nogueira D, Poblete M, Galindo-Domínguez H. Methodologies for teaching-learning critical thinking in higher education: the teacher’s view. Think Skills Creativity. 2019;33:100584.

Immonen K, Oikarainen A, Tomietto M, Kääriäinen M, Tuomikoski A-M, Kaučič BM, Perez-Canaveras RM. Assessment of nursing students’ competence in clinical practice: a systematic review of reviews. Int J Nurs Stud. 2019;100:103414.

Oermann MH, Gaberson KB, De Gagne JC, NPD-BC C. Evaluation and testing in nursing education. Springer Publishing Company; 2024.

McCarty T. (2020). How to Build Assessments for Clinical Learners. Roberts Academic Medicine Handbook: A Guide to Achievement and Fulfillment for Academic Faculty, 83–90.

Gholami M, Changaee F, Karami K, Shahsavaripour Z, Veiskaramian A, Birjandi M. Effects of multiepisode case-based learning (CBL) on problem-solving ability and learning motivation of nursing students in an emergency care course. J Prof Nurs. 2021;37(3):612–9.

King N. (2016, April). Case-based exams for learning and assessment: Experiences in an information systems course [ Confeence presentation]. In 2016 IEEE Global Engineering Education Conference (EDUCON) , Abu Dhabi, UAE.

Pereira D, Flores MA, Niklasson L. Assessment revisited: a review of research in Assessment and evaluation in Higher Education. Assess Evaluation High Educ. 2016;41(7):1008–32.

Creswell JW, Poth CN. Qualitative inquiry and research design: choosing among five approaches. SAGE; 2016.

O’Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research. Acad Med. 2014;89(9):1245–51.

Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol. 2013;13(1). https://doi.org/10.1186/1471-2288-13-117

Fetters MD, Curry LA, Creswell JW. Achieving integration in mixed methods designs—principles and practices. Health Serv Res. 2013;48(6pt2):2134–56. https://doi.org/10.1111/1475-6773.12117

Liu L, Li M, Zheng Q, Jiang H. The effects of case-based teaching in nursing skill education: cases do matter. INQUIRY. J Health Care Organ Provis Financing. 2020;57:004695802096442.

Bonney KM. Case study teaching methods improve student performance and perceptions of learning gains. J Microbiol Biology Educ. 2015;16(1):21–8.

Rezaee R, Mosalanejad L. The effects of case-based team learning on students’ learning, self-regulation, and self-direction. Global J Health Sci. 2015;7(4):295.

Harman T, Bertrand B, Greer A, Pettus A, Jennings J, Wall-Bassett E, Babatunde OT. Case-based learning facilitates critical thinking in undergraduate nutrition education: students describe the big picture. J Acad Nutr Dietetics. 2015;115(3):378–88.

Nunohara K, Imafuku R, Saiki T, Bridges SM, Kawakami C, Tsunekawa K, Niwa M, Fujisaki K, Suzuki Y. (2020). How does video case-based learning influence clinical decision-making by midwifery students? An exploratory study. BMC Med Educ, 20 (1).

Download references

Acknowledgements

The authors wish to thank the nursing students at SQU who voluntarily participated in this study.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and affiliations.

Sultan Qaboos University, Al Khodh 66, Muscat, 123, Oman

Basma Mohammed Al Yazeedi, Lina Mohamed Wali Shakman, Sheeba Elizabeth John Sunderraj, Harshita Prabhakaran, Judie Arulappan, Erna Judith Roach, Aysha Al Hashmi & Zeinab Al Azri

Oman College of Health Science, Norht Sharqia Branch, Ibra 66, Ibra, 124, Oman

Aysha Al Hashmi

You can also search for this author in PubMed   Google Scholar

Contributions

Dr. Basma Mohammed Al Yazeedi contributed to conceptualization, methods, data collection, data analysis, writing the draft, and reviewing the final draft. Ms. Lina Mohamed Wali Shakman contributed to conceptualization, data collection, data analysis, writing the draft, and reviewing the final draft. Ms. Sheeba Elizabeth John Sunderraj contributed to conceptualization, methods, data collection, writing the draft, and reviewing the final draft.Ms. Harshita Prabhakaran contributed to conceptualization, data collection, writing the draft, and reviewing the final draft.Dr. Judie Arulappan contributed to conceptualization and reviewing the final draft.Dr. Erna Roach contributed to conceptualization writing the draft and reviewing the final draft.Ms. Aysha Al Hashmi contributed to the conceptualization and reviewing the final draft. Dr. Zeinab Al Azri contributed to data collection, data analysis, writing the draft, and reviewing the final draft.All auhors reviewed and approved the final version of the manuscirpt.

Corresponding author

Correspondence to Zeinab Al Azri .

Ethics declarations

Ethics approval and consent to participate.

The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Research and Ethics Committee of the College of Nursing, Sultan Qaboos University SQU (CON/NF/2023/18). All data was held and stored following the SQU data policy retention. Informed consent to participate was obtained from all of the participants in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1:

The questionnaire used in this study is attached as a supplementary document.

Supplementary Material 2

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Yazeedi, B.M.A., Shakman, L.M.W., Sunderraj, S.E.J. et al. Perceived efficacy of case analysis as an assessment method for clinical competencies in nursing education: a mixed methods study. BMC Nurs 23 , 441 (2024). https://doi.org/10.1186/s12912-024-02102-9

Download citation

Received : 07 April 2024

Accepted : 17 June 2024

Published : 28 June 2024

DOI : https://doi.org/10.1186/s12912-024-02102-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Case-analysis
• Clinical competency
• Nursing education

BMC Nursing

ISSN: 1472-6955

• General enquiries: [email protected]