suggested research topics in medical laboratory science in the philippines

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Attitude, Knowledge, and Practices on Basic Life Support among Medical Laboratory Science Interns of Selected Universities in Metro Manila, Philippines

2021, International Journal of Progressive Research in Science and Engineering

Related Papers

Rogelio Velasco Jr.

Objective. This study aimed to assess the knowledge, attitude, and practice on the Basic Life Support (BLS) among the Philippine General Hospital medical interns, compare the scores between trained and untrained interns, and determine the proportion of respondents with updated BLS training. Methods. This was a cross-sectional study using a thirty-five item questionnaire on the knowledge, attitude, and practice of medical interns at the Philippine General Hospital on the BLS. Results. A total of 262 interns were included in the study. Although the mean knowledge scores of untrained and trained medical interns did not differ significantly (7.00 vs. 7.30, p=0.1637), more than 20% committed mistakes on questions on the following: jaw-thrust, rescue breaths, defibrillation, and CPR check. Generally, interns followed the BLS guidelines in their practice and had a positive attitude towards BLS. However, only 37% of trained participants had training within the past two years. Conclusion. Mo...

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Rhea Rowena Apolinario

Presents the perceptions of the BLIS graduates of UP SLIS from 1997 to 2011 as to the relevance and sufficiency of the BLIS program. Also utilizes their perceptions to provide inputs in building an Alumni Support Community.

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bronwyn beovich

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Janaki Medical College Journal of Medical Science

Nashib Pandey

Background and Objectives: Survival after cardiopulmonary arrest depends on early and effective cardiopulmonary resuscitation (CPR). Demand for courses of basic life support (BLS)/CPR is increasing worldwide. Till date, there have been little reports regarding CPR knowledge and attitude among the Nepalese dental health personnel. This study aimed to assess the knowledge and attitude towards CPR among undergraduate dental students, interns and postgraduate residents studying at Kantipur Dental College and Teaching Hospital (KDCH), Kathmandu, Nepal. Material and Methods: A descriptive cross-sectional study was conducted among 243 dental students studying at KDCH. Data was collected through a self -administered semi-structured questionnaire. Collected data was organized, recorded, coded and then analyzed using Statistical Package of Social Science (SPSS) version 16. Descriptive statistics was used to compute mean, percentile, standard deviation and one way ANOVA test was done to compar...

GSC Biological and Pharmaceutical Sciences

GULA, AILA MARIE

The immunization practice of pharmacists has only been recently approved in the Philippines; thus, it is imperative to assess their engagement using the Knowledge, Attitudes, Practices (KAP) model. A descriptive quantitative-correlational study that utilized a convenience sampling technique was conducted with 90 respondents. A 49-item self-made questionnaire was employed through Google forms. The data collected was subjected to various statistical treatments for descriptive and correlational analysis. Results showed that respondents have a high level of knowledge regarding the prevention of Adverse Events Following Immunization (3.81), and critical thinking and decision-making upon its occurrence (3.92). The respondents' attitudes also showed high levels of agreement toward the promotion of immunization (3.89) and patient counseling (3.97). Moreover, the majority of the respondents administered vaccines (87.78%) and COVID-19 was the vaccine given the most (77.78%). Their respons...

Prof. Negar Elhamian , Helen Bihag , Dondon Salingay

International Journal of Progressive Research in Science and Engineering

Yves Amigable

De La Salle Araneta University

Gary Regala

Strategic Intervention Material (SIM) for Grade Six - A Journey to the Center of Earthquake was used to assess the ability of the learners to understand the earthquake as a force that affects changes on the earth’s surface and demonstrate what to do before, during and after earthquake to improve learning outcomes. The one sample quasi-experimental design was used among five hundred seventy nine (579) student participants randomly selected from fifteen (15) schools in the Division of City Schools of Manila. The study revealed that student engagement with the strategic intervention material enhances students’ learning based on pre-test mean score of 6.51 to post-test mean score of 8.04 with the p-value of < 0.001 means that there is a significant difference between the pre-test and post-test performance of the pupils. This agrees with the previous indication that the students did better in their post-test than in pre-test. The results of the post-test scores were significantly higher than the pre-test scores of the pupils with t-value of -29.04 and df of 578. This showed that SIM is an effective teaching and learning approach, particularly when it is used to assess the ability of the learners to understand the earthquake as a force that affects changes on the earth’s surface and demonstrate what to do before, during and after earthquake. A proposed Strategic Intervention Material for Grade Six - A Journey to the Center of Earthquake was developed to strengthen the performance of the learners. Regala, G. Z. (2020). Strategic Intervention Material for Grade Six A Journey to the Center of Earthquake. 2nd International Multidisciplinary Research Congress - De La Salle Araneta University - Malabon City. Available at https://www.dlsau.edu.ph/research/congresses/files/5th-Research-Congress-(2nd%20International%20Research%20Congress)-Book-of-Abstracts.pdf

Kashif Sohail

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suggested research topics in medical laboratory science in the philippines

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Medical laboratory science and nursing students' perception of academic learning environment in a Philippine university using Dundee Ready Educational Environment Measure (DREEM)

Affiliation.

1 Department of Medical Laboratory Science, Saint Louis University School of Natural Sciences, Baguio, Philippines.
PMID: 27649901
PMCID: PMC5066071
DOI: 10.3352/jeehp.2016.13.33

Purpose: This study aimed to compare the perception of the academic learning environment between medical laboratory science students and nursing students at Saint Louis University, Baguio City, Philippines.

Methods: A cross-sectional survey research design was used to measure the perceptions of the participants. A total of 341 students from the Department of Medical Laboratory Science, School of Natural Sciences, and the School of Nursing answered the Dundee Ready Education Environment Measure (DREEM) instrument from April to May 2016. Responses were compared according to course of study, gender, and year level.

Results: The total mean DREEM scores of the medical laboratory science students and nursing students did not differ significantly when grouped according to course of study, gender, or year level. Medical laboratory science students had significantly lower mean scores in the sub-domains 'perception of learning' and 'perception of teaching.' Male medical laboratory science students had significantly lower mean scores in the sub-domain 'perception of learning' among second year students. Medical laboratory science students had significantly lower mean scores in the sub-domain 'perception of learning.' Nursing students identified 7 problem areas, most of which were related to their instructors.

Conclusion: Medical laboratory science and nursing students viewed their academic learning environment as 'more positive than negative.' However, the relationship of the nursing instructors to their students needs improvement.

Keywords: Cross-sectional studies; Medical laboratory science; Nursing students; Perception; Philippines.

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The Master of Science in Medical Laboratory Science is designed to produce scholars and high-level professionals in the field of laboratory medicine who demonstrate the professional competencies and ethical standards required in the local and global workplace. It seeks to hone the professional skills of its graduates that will enable them to become leaders in their careers and community and the process improve the delivery of health care services and ensure the continued growth and development of the profession.

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The complete program curriculum for Master of Science in Medical Laboratory Science

Program Educational Objectives (PEO)
Student Outcomes (SO)

3-5 years after graduation, alumni of MSMLS shall:

Have participated in the continuous development of laboratory professionals with enhanced competencies in the performance of their professional duties and responsibilities.
Have practiced high professional, technical and ethical standards and quality of practice in the field of laboratory medicine.
Have assumed leadership and supervisory roles in clinical laboratories and related fields.
Have kept abreast with the latest trends in the profession brought about by modernization and technological advancement particularly in the following fields of specialization.

By the time of graduation, Master of Science in Medical Laboratory Science students shall have the ability to:

perform Medical Laboratory Science professional practice with accuracy and precision
apply interpersonal, communication and leadership skills required in the practice of the profession;
practice effective critical thinking and problem-solving skills;
engage in research studies that will contribute to the upliftment of the profession;
continuously update oneself through continuing professional education and training.

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Recent Publications

Alvero-Bascos EM, Rodriguez LJV, Duya MV, Fernando ES, Ong PS. 2019. Philippine Rafflesia : Emerging patterns in floral morphology and distribution. Flora 257: 151409.
Santos BS, Basiao ZU, Quilang JP. 2019. Genetic diversity and patterns of demographic expansion in natural populations of milkfish, Chanos chanos (Forsskål, 1775), in the Philippines. Mitochondrial DNA Part A; DNA Mapping, Sequencing and Analysis 30: 312-324. DOI:10.1080/24701394.2018.1504931.
Ragasa LRP, Dinglasan JLN, Felipe IRE, Basiao ZU, Velarde MC. 2019. Exposure to Aeromonas hydrophila induces inflammation and increases expression of the gene encoding for a putative dual CTLD-containing lectin in milkfish liver. Comp Biochem Physiol B Biochem Mol Biol. 230:37-47. doi: 10.1016/j.cbpb.2019.01.008.
Fornillos, R.J.C., Fontanilla, I.K.C., Chigusa, Y., Kikuchi, M., Kirinoki, M., Kato-Hayashi, N., Kawazu, S., Angeles, J.M., Tabios, I.K., Moendeg, K., Goto, Y., Tamayo, P.G., Gampoy, E.F., Pates, I., Chua, J.C. and Leonardo, L.R.. 2019. Infection rate of Schistosoma japonicum in the snail Oncomelania hupensis quadrasi in endemic villages in the Philippines: Need for snail surveillance technique. Trop. Biomed., 36, 402–411.
Denusta PJT, De Jesus-Ayson EG, Laron MA, Aya FA, Garcia LMB. 2019. Gonad development and size‐at‐maturity of silver therapon Leiopotherapon plumbeus (Kner 1864; Teleostei:Terapontidae) in tropical volcanic lakes in south Luzon, Philippines. Journal of Applied Ichthyology 35: 933-943.
Sato, M.O., Adsakwattana, P., Fontanilla, I.K.C., Kobayashi, J., Sato, M., Pongvongsa, T., Fornillos, R.F.C. 2019. Odds, challenges and new approaches in the control of helminthiasis, an Asian study. Parasite Epidemiology and Control, 4, e00083.
Abdulmalik-Labe OP, Quilang JP. 2019. DNA barcoding of fishes from Lake Lanao, Philippines. Mitochondrial DNA Part B; Resources 4(1): 1890-1894.
Peralta, E.M., Belen, A.E., Buenaventura, G.R., Cantre, F.G.G., Espiritu, K.G.R., De Vera, J.N.A., Perez, C., Tan, A.K.V., De Jesus, I.B.B., Palomares, P.Q., Briones, J.C.A., Ikeya, T., Magbanua, F.S., Papa, R.D.S., & Okuda, N. (2019). Stream benthic macroinvertebrate assemblages reveal the importance of the recently established protected freshwater area in a tropical watershed. Pacific Science, 73(3): 305-320.
Abdulmalik-Labe OP, Quilang JP. 2019. Genetic diversity among the endemic barb Barbodes tumba (Teleostei: Cyprinidae) populations from Mindanao, Philippines. Journal of Threatened Taxa 11(7): 13822-13832.
Li, H.M., Guinto, S.K.P., Papa, R.D.S., Han, B.-P., Magbanua, F.S., Rizo, E.Z.C., Dumont, H.J. & Lin, Q. 2019. On Filipinodiaptomus Lai, Mamaril, Sr. & Fernando, 1979 (Calanoida: Diaptomidae): Redescription and re-allocation of the Philippine endemic Diaptomus vexillifer Brehm, 1933. Crustaceana, 92(5): 513-536.
Magbanua, F.S., Salluta, J.C.R., Deborde, D.D.D. & Hernandez, M.B.M. (2019). Benthic macroinvertebrates of the University of the Philippines Diliman campus waterways and their variation across land use in an urban, academic landscape. Science Diliman, 31(1): 5-24.
Claveria RJR, Perez TR, Apuan MJB, Apuan DA, Perez REC.2019. Pteris melanocaulon Fée is an As hyperaccumulator. Chemosphere 236: 124380.
Ibana, JA, Sherchand, SP, Fontanilla, FL, Nagamatsu, T, Schust, DJ, Quayle, AJ, & Aiyar, A. 2018. Chlamydia trachomatis -infected cells and uninfected-bystander cells exhibit diametrically opposed responses to interferon gamma. Scientific Reports 8, 8476. http://doi.org/10.1038/s41598-018-26765-y
Matias, A. M. A., & Riginos, C. (2018). Revisiting the “Centre Hypothesis” if the Indo-West Pacific idiosyncratic genetic diversity of nine reef species offers weak support for the coral triangle as a centre of genetic biodiversity. Journal of Biogeography, 45, 1806–1812.
Obusan M.C.M., Aragones L.V., Rivera, W.L., & Siringan M.A.T. (2018). Antibiotic susceptibility patterns of bacteria isolated from cetaceans stranded in the Philippines. Aquatic Mammals 2018, 44(5), 568-579, DOI 10.1578/AM.44.5.2018.568
Matias, A. M., & Hereward, J. (2018). The complete mitochondrial genome of the five-lined cardinalfish Cheilodipterus quinquelineatus (Apogonidae). Mitochondrial DNA Part B: Resources, 3(2), 521–522.
Victoria AJ, Cao EP , Salmaso N, Segata N and Donati C. 2018. Draft genome sequence of the cadmium-resistant strain JJU2, belonging to Family Hapalosiphonaceae of the cyanobacteria. Microbiology Resource Announcements 7:e00876-18.
Imperial, I. C. V. J., Ibana, J. A. (2018). Section 1-Genetics of Acquired Antimicrobial Resistance in Animal and Zoonotic Pathogens. In A. Cloeckaert, M. S. Zygmunt, B. Doublet (Eds.) The Global spread of antibiotic resistant bacteria in the “One Health” perspective . Frontiers Media SA ISBN: 9782889453948 Frontiers in Microbiology , 7, 1983.
Vocal, I.C.S., Perez, T.R., Magbanua, F.S. & Hernandez, M.B.M. (2018). The use of epilithic diatom assemblages in assessing land use in Silago, Southern Leyte, Philippines. Philippine Journal of Systematic Biology, 12(2): 92-102.
Hogan JA, Zimmerman JK, Thompson J, Uriarte M, Swenson NG, Condit R, Hubbell S, Johnson DJ, Sun IF, Chang-Yang C-H, Su S-H, Ong P, Rodriguez L, Monoy CC, Yap S, Davies SJ. 2018. The frequency of cyclonic wind storms shapes tropical forest dynamism and functional trait dispersion. Forests 9:404.
Catap ES, Kho MJL, Jimenez MRR. 2018. In vivo nonspecific immunomodulatory and antispasmodic effects of common purslane ( Portulaca oleracea Linn.) leaf extracts in ICR mice. Journal of Ethnopharmacology 215: 191–198.
Compton SG, Chen X-Y, Chen Y, Hatcher MJ, Peng Y-Q, Quinnell RJ, Rodriguez LJ, Yu H, Ouyang A, Wei F-L, Cai Z-T, Wang R. 2018. Host-parasitoid relationships within ﬁgs of an invasive ﬁg tree: a ﬁg wasp community structured by gall size. Insect Conservation and Diversity 11:341-351.
Batomalaque GA , and Rosenberg G. 2018. Three new species of Zaptyx , (Gastropoda: Eupulmonata: Clausiliidae) from the Babuyan and Batanes Islands, Philippines. Archiv Fur Molluskenkunde, 147(1), 77–86.
Anticamara JA and Tan CB. 2018. Survival and growth of re-attached storm-generated coral fragments post super-typhoon Haiyan (a.k.a. Yolanda). Science Diliman. Vol. 30 Issue 2, p5-31. 27p.
Ramos DAE, Batomalaque GA, and Anticamara JA. 2018. Current status of Philippine mollusk museum collections and research, and their implications on biodiversity science and conservation. Philippine Journal of Science. Vol. 147 (No. 1).
Romana-Eguia MR, Santos BS, Ikeda M, Basiao ZU, Kijima A. 2018. Genetic assessment of milkfish ( Chanos chanos Forsskal) stocks based on novel short tandem repeats for marker‐aided broodstock management. Aquaculture Research. 49(4):1557-68.
Subejano MSEP, PENULIAR GM . 2018. Prevalence and risk factors for colonization of Campylobacter spp. in household dogs in Metro Manila, Philippines. Tropical Biomedicine. 35(3):1-11.
Ayala MGB, Cabrera EC, PENULIAR GM . 2018. Methicillin-resistant Staphylococcus aureus carriage in a residential care institution for the elderly in Quezon City, Philippines. Philippine Science Letters. Volume 11 (Supplement): 4-11.
Serrano GL, PENULIAR GM . 2018. Prevalence, Antibiogram and Virulence Gene Profiles of Uropathogenic Escherichia coli from Pregnant Women with Urinary Tract Infection. Philippine Science Letters. Volume 11 (Supplement): 46-55.
Daquioag JEL, Almirol RBC, Ayala MGB, Subejano MSEP, PENULIAR GM . 2018. Staphylococcus aureus and Methicillin-resistant S. aureus carriage in Public Computer Service Providers and Utility Jeepneys in UP Diliman. Science Diliman. 30(1): 60-73.
Pleto, JV, Arboleda, MDM, Simbahan, JF and Migo, VP. 2018. Assessment of the Effect of Remediation Strategies on the Environmental Quality of Aquaculture Ponds in Marilao and Meycauayan, Bulacan, Philippines. Journal of Health and Pollution. 8(20):1-14. DOI: 10.5696/2156-9614-8.20.181205
Perez C.M.T., Pajares I.G., Alcantara V.A., Simbahan J.F. 2018. Detection of bacterial laminarinase activity for possible application in ethanol production from algal hydrolysate of brown algae, Sargassum sp., using halotolerant yeast. Biofuel Research Journal. 17: 792-797. DOI: 10.18331/BRJ2018.5.1.6
Velarde MC. 2017. Epidermal Barrier Protects against Age-Associated Systemic Inflammation. J Invest Dermatol. 137(6):1206-1208. doi: 10.1016/j.jid.2017.02.964.
Rodriguez LJ, Bain A, Chou LS, Conchou L, Cruaud A, Gonzales R, Hossaert-McKey M, Rasplus JY, Tzeng HY, Kjellberg F. 2017. Diversification and spatial structuring in the mutualism between Ficus septica and its pollinating wasps in insular South East Asia. BMC Evolutionary Biology 17:207. doi.org/10.1186/s12862-017-1034-8
Fontanilla IKC, Naggs F, Wade CM. 2017. Molecular phylogeny of the Achatinoidea (Mollusca: Gastropoda). Molecular Phylogenetics and Evolution 114: 382-385.
Albritton, H.L., Kozlowski, P.A., Lillis, R.A., McGowin, C.L., Siren, J.D., Taylor, S.N., Ibana, J.A., Buckner, L.R., Shen, L. and Quayle, A.J., 2017. A novel whole-bacterial enzyme linked-immunosorbant assay to quantify Chlamydia trachomatis specific antibodies reveals distinct differences between systemic and genital compartments. PLoS One, 12(8), p.e0183101.
Marquez, C.M.D., Ibana, J.A. and Velarde, M.C. 2017. The female reproduction and senescence nexus. American Journal of Reproductive Immunology, 77(5).
Ibana JA. Towards Excellence in Science Research: A UP Research Guidebook, Copyright 2017 University of the Philippines “Chapter 6 : Gaining competency in science research” page 96.
Ibana, J., Romero, M. and Michael, A.N., 2017. P-328 Therapeutic Effects of 1-Methyl Tryptophan and Lactobacillus spp. on AOM/DSS-Induced Institute of Cancer Research (ICR) Mouse Model. Inflammatory Bowel Diseases, 23, pp.S104-S105.
Santos BS, Canoy RJC, Tango-Imperial JM, Quilang JP. 2017. Length – weight and length – length relationships, condition factor, sex ratio and gonadosomatic index of the ariid catfishes Arius dispar and Arius manillensis (Siluriformes: Ariidae) in Laguna de Bay, Philippines. Philippine Journal of Science 146(1): 85-94.
Rodriguez LJ, Young F, Rasplus J-Y, Kjellberg F, Compton SG. 2017. Constraints on convergence: hydrophobic hind legs allow some male pollinator fig wasps early access to submerged females. Journal of Natural History 51, 761-782. http://dx.doi.org/10.1080/00222933.2017.1293746
Cruaud P, Rasplus J-Y, Rodriguez LJV, Cruaud A. 2017. High throughput sequencing of multiple amplicons for barcoding and integrative taxonomy. Scientific Reports 7, 41948
Tamayo NC, Anticamara JA , and Acosta-Michlik L. 2017. National estimates of values of Philippine reef’s ecosystem services. Ecological Economics 146: 633.
Tan CB and Anticamara JA . 2017. Trophic modeling of degraded reefs in Leyte Gulf, Philippines in the face of climate change and human-induced disturbances. Climate Disaster and Development Journal, Vol. 3 Issue 1.
Anticamara JA and Go KTB. 2017. Impacts of super-typhoon Yolanda on Philippine reefs and communities. Regional Environmental Change. 17: 703.
Bugayong MLG, Jacinto SD. 2017. In-vitro Cytotoxicity of Wrightia pubescens (Blanco) Merr., Aphanamixis polystachya (Wall.) Parker, and Platymitra arborea (Blanco) against selected human cancer cell lines. International Journal of Biosciences 11(5): 204-213.
Peralta, E.M., Guerrero, H.J.A., Arce, C.G.S.M., Domingo, J.J.A., Maute, M.A., San Miguel, M.D.S., Triño, E.M.C., De Jesus, I.B.B., Briones, J.C.A., Magbanua, F.S., Okuda, N. & Papa, R.D.S. (2017). Prevailing environmental conditions influence mollusk diversity and distribution around Talim Island of Laguna de Bay (Luzon Is., Philippines). The Antoninus Journal, 2017(1):31-39.
Magbanua, F.S., Fontanilla, A.M., Ong, P.S. & Hernandez, M.B.M. (2017). 25 years of freshwater research in the Philippines: what has been done and what to do next? Philippine Journal of Systematic Biology 11(1):1-15.
Sanchez, L.R.S., Magbanua, F.S. & Cao, E.P. (2017). Isolation, characterization and rapid screening of copper-tolerant cyanobacteria from mining sites, a strawberry farm and a forest site in Benguet Province, Philippines. Journal of the International Society for Southeast Asian Agricultural Sciences, 23(2):8-19.
Lopez, M.L.D., Magbanua, F.S., Mamaril, A.C. & Papa, R.D.S. (2017). Variations in microcrustacean (Crustacea: Cladocera, Copepoda) assemblages from selected groundwater-dependent ecosystems in the greater Luzon and Mindoro Island faunal regions (Philippines): insights to tropical groundwater ecology. Inland Waters, 7(4):428-439.
Buhian WPC, Rubio RO, Puzon JJM. 2017. Chromatographic fingerprinting and free-radical scavenging activity of ethanol extracts of Muntingia calabura L. leaves and stems. Asian Pacific Journal of Tropical Biomedicine 7(2): 139-143.
Vallejo B Jr, Conejar-Espedido J, Manubag L, Artiaga KCC, Damatac AM II, Imperial IVJ, Itong TAB, Fontanilla IKC, Cao EP. 2017. First record of the Charru mussel Mytella charruana d’Orbignyi, 1846 (Bivalvia: Mytilidae) from Manila Bay, Luzon, Philippines. BioInvasions Records 6(1): 49-55.
Dumilag RV, Aguinaldo Z-ZA, Mintu CB, Quinto MB, Ame CE, Andres RC, Yap SL, Cao EP, Vital PG, Fontanilla IKC, Monotilla WD. 2017. A review of the current taxonomic status of foliose Bangiales (Rhodophyta) in the Philippines. Phytotaxa 312(1): 47-59.
Licuanan AM, Duya MRM, Ong PS, Fontanilla IKC. 2017. DNA barcoding, population genetics, and phylogenetics of the illegally hunted Philippine Duck Anas luzonica (Aves: Anseriformes: Anatidae). Journal of Threatened Taxa 9(5): 10141–10150.
Pagoso EJA, Rivera WL. 2017. Cryptosporidium species from common edible bivalves in Manila Bay, Philippines. Marine Pollution Bulletin. 119: 31-39. DOI 10.1016/j.marpolbul.2017.03.005
De La Peña LBRO, Pagoso EJA, Rivera WL. 2017. Characterization of Cryptosporidium isolated from Asian green mussels in wet markets of Quezon City, Philippines. Philippine Agricultural Scientist. 100: S45-S54.
Adao DE, Herrera CMT, Galarion LH, Bolo NR, Carlos RS, Carlos ET, Carlos SS, Rivera WL. 2017. Detection and molecular characterization of Hepatozoon canis, Babesia vogeli, Ehrlichia canis, and Anaplasma platys in dogs from Metro Manila, Philippines. Korean Journal of Veterinary Research. 57: 79-88. DOI 10.14405/kjvr.2017.57.2.79
Calayag AMB, Paclibare PAP, Santos PDM, Bautista CA, Rivera WL. 2017. Molecular characterization and antimicrobial resistance of Salmonella enterica from swine slaughtered in two different types of Philippine abbatoir. Food Microbiology. 65: 51-56.
Paraoan EC, Rivera WL, Vital PG. 2017. Detection of class I and II integrons for the assessment of antibiotic and multidrug resistance among Escherichia coli isolates from agricultural irrigation waters in Bulacan, Philippines. Journal of Environmental Science and Health, Part B. 52: 306-313.
Paclibare PAP, Calayag AMB, Santos PDM, Rivera WL. 2017. Molecular characterization of Salmonella enterica isolated from raw and processed meats from selected wet markets in Metro Manila, Philippines. Philippine Agricultural Scientist. 100: S55-S62.
Vital PG, Caballes MB, Rivera WL. 2017. Antimicrobial resistance in Escherichia coli and Salmonella spp. Isolates from fresh produce and the impact to food safety. Journal of Environmental Science and Health, Part B. doi: 10.1080/03601234.2017.1331676.
Chua JC, Fontanilla IKC, Tabios IKB, Tamayo PG, De Chavez ERC, Agatsuma T, Kikuchi M, Kato-Hayashi N, Chigusa Y, Fornillos RJC, Leonardo LR. 2017. Genetic comparison of Oncomelania hupensis quadrasi (Mollendorf, 1895) (Gastropoda: Pomatiopsidae), the intermediate host of Schistosoma japonicum in the Philippines based on 16S ribosomal RNA sequence. Science Diliman 29(2): 32-50.
Moendeg KJ, Angeles JMM, Nakao R, Leonardo LR, Fontanilla IKC, Goto Y, Kirinoki M, Villacorte EA, Rivera PT, Inoue N, Chigusa Y, Kawazu S. 2017. Geographic strain differentiation of Schistosoma japonicum in the Philippines using microsatellite markers. PLoS Neglected Tropical Diseases 11(7): e0005749.
Oponda, Cecille Vienne L., Brian S. Santos, and Zubaida U. Basiao. 2017. Morphological differences in five strains of genetically improved Nile tilapia ( Oreochromis niloticus ) using geometric morphometrics. J. ISSAAS 23(1): 44-55.
Lim PWN, Tiam-Lee DC, Paclibare PAP, Subejano MSEP, Cabero-Palma JAS, PENULIAR GM . 2017. High rates of contamination of poultry meat products with drug resistant Campylobacter in Metro Manila, Philippines. Japanese Journal of Infectious Diseases. 70(3): 311-313.
Nolissa Delmo-Organo, Evelyn F. Delfin, Glenn B. Gregorio, Nacita B. Lantican, Jessica F. Simbahan and Erlinda S. Paterno. 2017. Combined Effects of Salinity, Rice Variety and Ricegrowth Stage on the Diversity of Bacterial Communities Associated With Rice ( Oryza sativa L.). J. ISSAAS Vol. 23, No. 2: 134-145
Fredmoore L. Orosco, Sean M. Estrada, Jessica F. Simbahan, Virgie A. Alcantara and Irene G. Pajares. 2017. Genome Shuffling for Improved Thermotolerance, Ethanol Tolerance and Ethanol Production of Saccharomyces cerevisiae 2013. Phil Sci Letters. 10(1)22-28
Molina, J., McLaughlin, W., Wallick, K., Pedales, R., Marius, V.M., Tandang, D.N., Damatac, A., Stuhr, N., Pell, S.K., Lim, T.M. and Novy, A., 2017. Ex Situ Propagation of Philippine Rafflesia in the United States: Challenges and Prospects. Sibbaldia: the Journal of Botanic Garden Horticulture, (15), pp.77-96.
Yap SL, Davies SJ, Condit R. 2016. Dynamic response of a Philippine dipterocarp forest to typhoon disturbance. Journal of Vegetation Science 27 (2016) 133–143
Anticamara JA, Go KTB. Spatio-temporal declines in Philippine fisheries and its implications to coastal municipal fishers’ catch and income. Frontiers in Marine Science.
Galindon JMM, Ong PS, Fernando ES (2016) Rafflesia consueloae (Rafflesiaceae), the smallest among giants; a new species from Luzon Island, Philippines. PhytoKeys 61: 37-46. doi:10.3897/phytokeys.61.7295
Shekelle M, S Gursky, S Merker & P Ong. 2015. Philippine Tarsier Carlito syrichta* (Linnaeus, 1758) Philippines (2014). pp. 43-44. In: C Schwitzer, RA Mittermeier, AB Rylands, F Chiozza, EA Williamson, J Wallis and A Cotton (eds.). Primates in Peril: The World’s 25 Most Endangered Primates 2014–2016, IUCN SSC Primate Specialist Group (PSG), International Primatological Society (IPS), Conservation International (CI), and Bristol Zoological Society, Arlington, VA
D.L. Valle, Jr., E.C. Cabrera, J.J.M Puzon, and W.L. Rivera. 2016. Antimicrobial Activities of Methanol, Ethanol and Supercritical CO2 Extracts of Philippine Piper betle L. On Clinical Isolates of Gram Positive and Gram Negative Bacteria with Transferable Multidrug Resistance. PloS ONE 11(1): e0146349. doi:10. 1371/journal.pone.0146349.
W.P.C. Buhian, R.O. Rubio, D.L. Valle, Jr., and J.J.M. Puzon. 2016. Bioactive Metabolite Profiles and Antimicrobial Activity of Ethanolic Extracts from Muntingia calabura L. Leaves an Stems. Asian Pacific Journal of Tropical Biomedicine (accepted February 2016).
J.J.M. Puzon, E.R. Tabayoyong, and H.J.M. Jumawan. 2015. Morpho-Physiological Changes in Euphorbia hirta L. In Response to Mercury Exposure. Philippine Journal of Science 144(2): 161-170.
Menon R, Behnia F, Polettini J, Saade GR, Campisi J, Velarde MC. 2016. Placental Membrane Aging and HMGB1 Signaling Associated with Human Parturition. Aging. 8(2):216-30
Valle DL, Paclibare, PAP, Cabrera, EC and Rivera, WL. 2016. Molecular and phenotypic characterization of methicillin-resistant Staphylococcus aureus isolates from a tertiary hospital in the Philippines. Tropical Medicine and Health 44:3. DOI 10.1186/s41182-016-0003-z
Adao, DEV and Rivera WL. 2016. Loop-mediated isothermal amplification (LAMP) assay for the rapid detection of the sexually-transmitted parasite, Trichomonas vaginalis. Annals of Parasitology 2016, 62(1), 25–31. doi: 10.17420/ap6201.28
Belleza, MLB, Reyes, JCB, Tongol-Rivera, PN, Rivera WL. 2016. Subtype analysis of Blastocystis sp. isolates from human and canine hosts in an urban community in the Philippines. Parasitology International 65 (2016) 291–294
Magbanua, F.S., Townsend, C.R., Hageman, K.J., Piggott, J.J., Matthaei, C.D. (2016) Individual and combined effects of fine sediment and glyphosate herbicide on invertebrate drift and insect emergence: a stream mesocosm experiment. Freshwater Science 35(1), 139-151.
Adorable-Asis, A.-G.A., Cauyan, G.A., Pagulayan, R.C., Magbanua, F.S., Papa, R.D.S. (2016) The macro-gastropod communities of aquaculture-intensive lakes in the Philippines. Molluscan Research, 1-8. DOI:10.1080/13235818.2016.1201016
Heaney, Lawrence Richard; Balete, Danilo S.; Duya, Mariano Roy M.; Duya, Melizar V.; Jansa, Sharon A.; Steppan, Scott J.; et al.(2016). Doubling diversity: a cautionary tale of previously unsuspected mammalian diversity on a tropical oceanic island. Frontiers of Biogeography, 8(2). fb_29667. Retrieved from: Doubling diversity: a cautionary tale of previously unsuspected mammalian diversity on a tropical oceanic island
Santos, D.M.C., C.F.F. Besa, A.J.A. Victoria and E.P. Cao. 2016. Simple sequence repeat analysis of selected NSIC-registered coffee varieties in the Philippines. Science Diliman 28: 1-13.
Velarde MC, Demaria M. 2016 Targeting Senescent Cells: Possible Implications for Delaying Skin Aging: A Mini-Review. Gerontology. DOI: 10.1159/000444877
Velarde MC, Menon R. 2016 Positive and negative effects of cellular senescence during female reproductive aging and pregnancy. Journal of Endocrinology. 230(2): R59-76.
Anderson-Teixeira KJ, Davies SJ, Bennett AC, Gonzalez-Akre EB, Muller-Landau HC, Wright SJ, Abu Salim K, AlmeydaZambrano AM, Alonso A, Baltzer JL, Basset Y, Bourg NA, Broadbent EN, Brockelman WY, Bunyavejchewin S, Burslem DFRP, Butt N, Cao M, Cardenas D, Chuyong GB, Clay K, Cordell S, Dattaraja HS, Deng X, Detto M, Du X, Duque Erikson DL, Ewango CEN, Fischer GA, Fletcher C, Foster RB, Giardina CP, Gilbert GS, Gunatilleke N, Gunatilleke S, Hao Z, Hargrove WW, Hart TB, Hau BCH, He F, Hoffman FM, Howe RW, Hubbell SP, Inman-Narahari FM, Jansen PA, Jiang M, Johnson DJ, Kanzaki M, Kassim AR, Kenfack D, Kibet S, Kinnaird MF, Korte L, Kral K, Kumar J, Larson AJ, Li Y, Li X, Liu S, Lum SKY, Lutz JA, Ma K, Maddalena DM, Makana R-R, Malhi Y, Marthews T, Serudin RM, McMahon SM, MCShea WJ, Memiaghe HR, Mi X, Mizuno T, Morecroft M, Myers JA, Novotny V, de Oliveira AA, Ong PS, Orwig DA, Ostertag R, den Ouden J, Parker GG, Phillips RP, Sack L, Sainge MN, Sang W, Sri-ngernyuang K, Sukumar R, Sun I-F, Sungpalee W, Suresh HS, Tan S, Thomas SC, Thomas DW, Thompson J, Turner BL, Uriarte M, Valencia R, Vallejo MI, Vicentini A, Vrška T, Wang X, Wang X, Weiblen G, Wolf A, Xu H, Yap S, Zimmerman J. 2015. CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Global Change Biology 21(2): 528-549 .
Anticamara JA, Go KTB, Madrid RGS, Ongsyping SS, Valdecanas FAT. 2015. National patterns of Philippine reef fish diversity and its implications on the current municipal-level management. Science Diliman (In Press)
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PMC10154750

Profiling low-proficiency science students in the Philippines using machine learning

Allan b. i. bernardo.

De La Salle University, Manila, Philippines

Macario O. Cordel, II

Marissa ortiz calleja, jude michael m. teves, sashmir a. yap, unisse c. chua, associated data.

The data analyzed in this study are available on the PISA 2018 Database page on the website of the Organisation for Economic Co-operation and Development at https://www.oecd.org/pisa/data/2018database/ , accessed on 17 Feb 2020.

Filipino students’ performance in global assessments of science literacy has always been low, and this was confirmed again in the PISA 2018, where Filipino learners’ average science literacy scores ranked second to last among 78 countries. In this study, machine learning approaches were used to analyze PISA data from the student questionnaire to test models that best identify the poorest-performing Filipino students. The goal was to explore factors that could help identify the students who are vulnerable to very low achievement in science and that could indicate possible targets for reform in science education in the Philippines. The random forest classifier model was found to be the most accurate and more precise, and Shapley Additive Explanations indicated 15 variables that were most important in identifying the low-proficiency science students. The variables related to metacognitive awareness of reading strategies, social experiences in school, aspirations and pride about achievements, and family/home factors, include parents’ characteristics and access to ICT with internet connections. The results of the factors highlight the importance of considering personal and contextual factors beyond the typical instructional and curricular factors that are the foci of science education reform in the Philippines, and some implications for programs and policies for science education reform are suggested.

Introduction

Global concerns such as the ongoing COVID pandemic and climate change crisis underscore the importance of science and technology for providing sustainable and responsible strategies for global development. Yet in many parts of the world, students’ interest and achievement in science continue to decline (Fensham, 2008 ). The Philippines is one of those countries where students are observed to have low levels of science literacy for many years now (Martin et al., 2004 ; Talisayon et al., 2006 ). This pattern was confirmed when the Philippines participated for the first time in the Program for International Student Assessment (PISA) in 2018, where the results found Filipino 15-year-olds near the bottom of the ranking among 78 countries and territories (Organisation for Economic Cooperation and Development [OECD], 2019 a, 2019 b). Some Philippine studies have tried to understand low science achievement by looking at the curriculum (Belmi and Mangali, 2020 ; Cordon and Polong, 2020 ) and instruction (Sumardani, 2021 ). In this study, we used machine learning approaches to determine the most accurate predictive models that can identify the poorest-performing science students in the PISA 2018 sample. For the variables in the predictive model, we consider a range of variables in the student questionnaire of PISA that refer to the student’s home and family background, beliefs, goals, attitudes, perceptions, and school experiences. We focus on non-instructional and non-curriculum variables with the view of understanding the variables that identify the Filipino students who are most vulnerable to poor science learning.

Filipino students’ science literacy in PISA

The Philippines participated in PISA for the first time in 2018, with students’ answering the assessments in reading, mathematics, science, and global competencies. For science literacy assessment, the PISA 2018 Framework broadly defines science literacy as “the ability to engage with science-related issues, and with the ideas of science, as a reflective citizen” (OECD, 2019 a, 2019 b, p. 100). According to the PISA science framework, scientific literacy relies on a combination of knowledge and competencies that are applied to different contexts. Student performance was reported using seven levels of proficiency, with Level 6 being the highest level of proficiency and Level 2 as the minimum level of proficiency. Students who achieve Level 2 proficiency are able to demonstrate the ability to use basic or everyday knowledge to explain scientific phenomena in familiar contexts and to interpret simple data sets. This level of proficiency serves as a baseline or minimum evidence for science literacy.

There were 7233 15-year-old Filipino students who participated in the PISA 2018 cycle (OECD, 2019 a, 2019 b), where the Philippines ranked as one of the poor-performing countries in science. The country had an average score of 357 which is significantly below the OECD average score of 489 with boys and girls performing similarly (355 and 359 average performance, respectively). Only about 22% of these students achieved Science Literacy scores at Level 2 or higher. In comparison, an average of 78% of students from OECD countries reached Level 2 or higher in the science literacy assessment. Students at Level 2 or higher can recognize the correct interpretation for familiar scientific phenomena and can use such knowledge to identify, in simple cases, whether a conclusion is valid based on the data provided. The poor performance of Filipino students is reflected in the fact that around 77% of them did not reach the minimum proficiency level. At the lowest proficiency levels (1A and 1B), students are only able to use everyday content and procedural knowledge to explain simple or familiar phenomena. Their ability to understand data and to design scientific inquiry is highly limited (OECD, 2019 a, 2019 b).

The pattern of Filipino students’ performance in PISA 2018 matches their achievement in another international assessment, the Trends in International Mathematics and Science Study (TIMSS). Similar to PISA, TIMSS measures students’ ability to apply their knowledge in different content areas of science. Performance was evaluated using benchmarks, each with a corresponding scale score: Low (400), Intermediate (475), High (550), and Advanced (625) (Mullis et al., 2020 ). Fourth-grade Filipino students who participated in the TIMSS 2019 cycle achieved an average scale score of 249, the lowest in 58 participating countries with an overall average score of around 491. Only 19% of Filipino students achieved scores in the Low benchmark or higher, which implies that the overwhelming majority of Filipino students “show limited understanding of scientific concepts and limited knowledge of foundational science facts” (Mullis et al., 2020 , p. 107).

Such consistently poor achievement levels in science are very likely the results of a wide range of interacting factors. Previous research using PISA data has attempted to identify important factors that differentiate the performance of high and low high and low scorers in PISA. For example, to determine which factors contribute to the gap between high and low PISA science scores, Alivernini and Manganelli ( 2015 ) considered factors coming from country, school, and student levels. They applied a classification and regression tree analysis to the PISA 2006 data from 25 countries to identify the factors that predicted high (above Level 4) or low (below Level 2) proficiency. The strongest country-level predictor was teacher salary. At the school level, parental pressure on the school’s standards (for low teacher salaries) and school size (for high teacher salaries) predicted students’ PISA performance. At the student level, science self-efficacy and awareness of environmental issues determined whether a student would be a low or high performance in the PISA science assessment.

In this study, we employ a similar approach to studying the variables that might explain the poor performance of most Filipino students. We compare the group of poor-performing students with the group of better-performing students and consider variables related to the student’s family/home backgrounds, beliefs, goals, attitudes, perceptions, and school experiences. Instead of using statistical approaches, we use machine learning approaches to test models that best identify and distinguish the group of poor-performing students from the better-performing ones. Machine learning approaches have been proposed as complementary to statistical approaches (Lezhnina and Kismihok, 2022 ), particularly for purposes of handling very large numbers of variables in high-dimensional datasets (like those in the PISA) while avoiding convergence problems and for developing multidimensional complex models that may feature nonlinear relationships (Hilbert et al., 2021 ; Yarkoni and Westfall, 2017 ). Such machine learning approaches have been used to study science achievement in PISA 2015 (Chen et al., 2021 ), but the study focused on identifying the top performers, not the poor performers. Such approaches have been used to study the PISA 2018 data in other countries like China (Lee, 2022 ), Singapore (Dong and Hu, 2019 ), and the Philippines (Bernardo et al., 2021 , 2022 ), but these studies focused on predicting either performance in reading, mathematics, or the average across domains, and none so far, have focused on the PISA 2018 science results. The analytic approaches are discussed in the methods section. But we first consider the range of possible predictor variables suggested by the relevant literature and that were available in the PISA student questionnaire the Filipino students answered.

Predictors of science learning and achievement

Most studies on science education in the Philippines have focused on curriculum (Balagtas et al., 2019 ; Ely, 2019 ; Morales, 2017b ), knowledge, beliefs, and practices of science teachers (Bug-os et al., 2021 ; Macugay and Bernardo, 2013 ; Orbe et al., 2018 ; Walag et al., 2020 ), and beliefs and perceptions of science learning (Alonzo and Mistades, 2021 ; Bernardo et al., 2008 ; Magalong and Prudente, 2020 ; Montebon, 2014 ); typically such studies do not empirically establish any relationship with Filipino students’ science learning or achievement. But there are some studies that do identify some predictors of Filipino students learning and achievement in chemistry, biology, physics, or some specific science lessons. And these typically fall into two types of inquiries: (a) those that investigate the learning outcomes of particular instructional strategies (Antonio and Prudente, 2021 ; Francisco and Prudente, 2022 ; Magwilang, 2016 ; Morales, 2016 , 2017a ; Orozco and Yangco, 2016 ), and (b) those that looked into student motivations and other non-cognitive student level variables as predictors of learning and achievement (Bernardo, 2021 ; Bernardo et al., 2015 ; Ganotice and King, 2014 ; King and Ganotice, 2013 , 2014 ). In this study, we worked with variables from the student self-report questionnaire of PISA 2018, so we could not study instructional strategies (i.e., the first set of studies above), but we are able to study student-level variables similar to the latter group of studies that include motivation, self-beliefs and a host of other variables that relate to students family and home backgrounds, perceptions and attitudes related to their classroom and school experiences, and their goals and aspirations for after they finish high school. We consider what the research literature suggests about such variables below, starting with student-level variables that were included in the PISA 2018 self-report survey and that were found to be important predictors of science literacy in previous PISA research in different countries.

Student factors

Certain student characteristics have been shown to influence their performance in science or scientific literacy. Gender appears to be associated with scientific literacy, with boys performing better than girls in the 2015 PISA cycle (OECD, 2016 ), but the results of numerous other studies are mixed (Cutumisu and Bulut, 2017 ; Lam and Lau, 2014 ; Sun et al., 2012 ). Affective and motivational factors seem to be important correlates of science achievement in PISA; these factors include students’ enjoyment of science and perceived value of science (Ozel et al., 2013 ), positive motivations, interest, more sophisticated epistemic beliefs (Hofverberg et al., 2022 ; She et al. 2019 ), self-efficacy, intrinsic and instrumental motivations for learning science (Kartal and Kutlu, 2017 ; Mercan, 2020 ), having a growth mindset (Bernardo, 2021 , 2022 ; Bernardo et al., 2021 ), among others. Other motivation-related processes are also associated with science literacy achievement. These include students’ projective self-assessments of their own abilities and their future aspirations (Lee and Stankov, 2018 ), perseverance and willingness to solve problems (Cutumisu and Bulut, 2017 ), and use of metacognitive strategies (Akyol et al., 2010 ; Callan et al., 2016 ). Interestingly, students’ reading skills and reading strategies have also been associated with science achievement (Barnard-Brak et al., 2017 ; Caponera et al., 2016 ). The role of reading strategies is proposed to be important as science learning depends to an extent on students’ comprehension of scientific text (Cano et al., 2014 ; Kolić-Vrhovec et al., 2011 ) and this association seems particularly important when the students are learning science in a second language instead of their home language (Van Laere et al., 2014 ), which is the case with the Filipino students who participated in PISA 2018.

Family and home factors

The socioeconomic status (SES) of students’ families has been a consistent predictor of scores in PISA (Lam and Zhou, 2021 ; Lee and Stankov, 2018 ), and this is true in the domain of science (Sun et al., 2012 ). This variable has been unpacked and many other factors associated with SES have been identified as predictors of achievement in PISA. These SES-related factors include the educational attainment and occupation of their parents (Chen et al., 2021 ; Schulze and Lemmer, 2017 ). In one such study, researchers found that parents’ education had the largest indirect effect on children’s PISA test scores (Burhan et al., 2017 ). The influence of each parent’s education, however, appears to differ. A study that analyzed the PISA 2000 performance of 30 countries found that the mother’s educational attainment had a greater impact on students’ scores than the father’s educational attainment (Marks, 2008 ). Similar to education, parents’ occupations also predicted students’ learning outcomes. Students whose parents had a higher level of occupation were found to have higher scientific competencies than students whose parents were low-skilled workers (Chi et al., 2017 ). Another variable related to SES is the students’ access to information and communication technologies (ICT) at home, particularly ICT with access to the Internet. ICT availability and use positively predicted performance in various PISA assessments (Hu et al., 2018 ; Petko et al., 2017 ; Yoon and Yun, 2023 ). We also note that studies indicate SES seems to be associated with some student-level factors. For example, SES is strongly associated with feelings of school belonging (King et al., 2022 ).

Other than SES-related factors at home, parental involvement and family investment in children’s education also appear to influence students’ academic performance (Ho and Willms, 1996 ). Using data from a national survey of Chinese students’ science literacy, Wang et al. ( 2012 ) found that students’ high scores were associated with parents’ investment in their children’s education through the purchase of educational materials and other resources at home. A study of ninth-grade students in South Africa found that family experiences, such as the learning environment at home, were related to the student’s motivation to learn science (Schulze and Lemmer, 2017 ).

School factors

The school characteristics that have been shown to influence students’ scientific literacy performance include SES (or SES composition), school enrollment size, and location. Wang et al. ( 2012 ) found that certain school characteristics, namely school standing, having libraries and computer laboratories, good relationships between teachers and students, and funding for teacher training were associated with higher science literacy scores. School SES composition was found to be strongly associated with high scientific literacy scores of Australian students (McConney and Perry, 2010 ). Analysis of Hong Kong students’ PISA scores revealed that school SES composition partly explained differences in science achievement (Sun et al., 2012 ). Class size (Bellibaş, 2016 ; although see Lam and Lau, 2014 ) and school location (Topçu et al., 2014 ) are also predictors of science achievement.

Other than these school characteristics, students’ experiences and perceptions of their classroom and school environments also predict their achievement in PISA. In a study of the performance of Chinese students in the 2015 PISA, Huang ( 2020 ) found that reported experience of bullying in school was associated with achievement scores, and this relationship was medicated by the student’s sense of belonging in school. School disciplinary climate and quality of student-teacher relationship were significant predictors in particular countries (Shin et al., 2009 ); with the effect of disciplinary climate possibly having a more positive impact on students from low SES groups but the evidence across countries is mixed (Chi et al., 2018 ; Scherer, 2020 ).

The current study

The extant literature suggests that a wide range of factors at the student, family/home, and school level are likely predictors of science literacy, although some of these factors were shown to be important predictors in some countries but not all. In this study, we explore a range of such factors to inquire which best identifies the poor-performing Filipino students in contrast to the better-performing ones. The factors explored in the study are among those in the PISA 2018 student self-report survey that Filipino students answered.

Most education research that examines relationships among such variables applies statistical approaches. In such studies, correlations can show the linear relationship between each variable of poor and better-performing groups. In the current Philippines PISA 2018 dataset where we examine 85 variables as predictors, the possible correlations are over 7000 in number. For a more complex, nonlinear system with hundreds of variables that are not independent, we believe that it is best to use machine learning models. In contrast to the standard statistical approach, machine learning models capture the high-dimensional, possibly nonlinear, interrelations among a very large number of predictors (Hilbert et al., 2021 ; Yarkoni and Westfall, 2017 ), while identifying those most relevant to prediction. And, in order for the analysis to be more valid, we argue that the model should be optimal, in this case, the model with the best accuracy. For this study, we try out different machine-learning approaches to determine the best model to uncover the relationships between these variables.

The specific objective is to use machine learning approaches to determine the most accurate model that best identifies the Filipino students who performed at the lowest levels in the science domain of PISA 2018. We sought the best model that will indicate the factors that identify the students who are vulnerable to poor learning in science in the hope that the model will call the attention of Filipino educators to the non-instructional and non-curricular factors that contribute to poor learning in science among Filipino learners. The variables that were considered included student factors (e.g., motivations, self-beliefs, goals, aspirations), family/home factors (e.g., family SES, parents’ education and occupations, learning resources at home), and classroom/school factors (e.g., instruction time for science, teacher behaviors, perceived school environment, self-reported social experiences in school).

Our methodology for determining the best model that features the most important variables that identify the low-performing Filipino student in science is summarized in Fig. Fig.1, 1 , which shows the different phases of our data analysis. The first step is data preparation which entails data cleaning, that is, removal of variables with 100% missing data, identification, and imputation of entries with missing values, and variable scaling. Next is feature selection which involves the careful refinement of the list of variables that may contain negative suppressors. Then, machine learning model training follows to search for the best nonlinear prediction model. Finally, the machine learning model evaluation describes quantitatively the model performance and reports variable importance.

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To find the optimal computational model, the whole data processing pipeline is performed for different sets of hyperparameters, for different machine learning approaches.

The dataset

The data we used in the analysis were from the Philippine sample in the PISA 2018 data (publicly accessible at https://www.oecd.org/pisa/data/2018database/ ). PISA 2018 test items for the science subject measure the ability to engage with science-related issues as a thoughtful citizen (OECD, 2019 a, 2019 b). To assess this, the questions given are related to contexts , e.g. personal, local and global issues, both current and historical that require understanding in science and technology; to knowledge , e.g. content, procedural, and epistemic; and to competencies that exhibit the ability to explain phenomena scientifically, evaluate and design scientific inquiry, and interpret data and evidence scientifically. In addition to these, students answered background questionnaires about themselves, their homes, and the school and learning experiences. As discussed, these variables were considered in this study. The performance of students is estimated and reported as 10 plausible values with 0.88 reliability for the Philippine science data.

A two-stage stratified sampling design was followed to obtain the nationally representative sample: (a) 187 schools were randomly selected from the country’s 17 regions, with the number of schools selected proportional to the regional distribution of schools, (b) students were then randomly selected from each school. As mentioned in the introduction, the final sample was 7233 15-year-old students. From the database, we identified 85 variables that referred to student, home/family, classroom/school factors suggested by the extant literature as possible predictors of science literacy, which we measured using the first plausible value of science literacy (PV1SCIE).

Data preparation

As reported earlier, over 80% of the Filipino students who participated in PISA 2018 were found to have less than Level 2 proficiency in science. The detailed distribution of participants across the different proficiency levels is shown in Fig. Fig.2a. 2a . Because our goal is to identify the variables that are potentially influential in identifying the extreme poor performers in science, we decided to train a binary classification model that identifies these students and to study the variables that are important in this model prediction. For the binary classification, the sample data was divided into two categories; the (1) poor-performing students, who have proficiency at Level 1b and below, and (2) better-performing students, who have proficiency at Levels 1a, 2, and above. The data distribution for the two groups is shown in Fig. Fig.2b 2b .

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Normalized Science proficiency level distribution of students ( a ) and distribution of students with poor and better performance in science ( b ). Poor performance category is for those students who belong to Level 1b and below proficiency levels, otherwise, the students are assigned to the better performance category.

The samples were further trimmed down as part of the data preparation. Students with more than 50% of the total variables missing were dropped from the dataset, obtaining the final data distribution in Table Table1. 1 . Sampled randomly, around 80% of the data were used as the training samples for the model training while the unseen or remaining data were used as the test data.

Data distribution of train and test sets with 80–20 split.

Data split	Poor performance (Level ≤ 1b)	Better performance (Level ≥ 1a)	Total
Training data	2419	3297	5716
Test data	628	801	1429
Total	3047	4098	7145

Note the imbalance in the number of training samples for the good and poor-performing students.

To avoid bias in training the model, data balancing was conducted by oversampling the poor-performance samples and undersampling the better-performance samples. For the poor-performing category with 2419 samples, the Synthetic Minority Oversampling Technique or SMOTE algorithm (Chawla et al., 2002 ) was applied to increase the samples. The SMOTE first chooses a random sample from the minority class, for example, sample A. Then, it looks for its nearest neighbor of the same class, for example, sample B. The algorithm performs a convex combination of the two samples to produce the synthetic sample. For the better-performing category with 3297 samples, the Tomek Links (Tomek, 1976 ) algorithm is used to undersample the majority class. The algorithm removes the ambiguous samples from the majority class, which is the data from the majority class that is closest to a minority class data. The final number of training samples for each of the poor and better performance categories is 3214.

The list of variables was further refined by removing variables with 100% missing values (i.e., the questions were not included in the set of questions asked for Filipino students). Those remaining variables with missing values were imputed using the k -nearest neighbor algorithm, where k = 7. Also, initial experiments showed the occurrences of negative suppressors. To minimize the number of suppressors, we removed variables with high correlation with other factors, i.e. | ⍴ | > 0.75. Finally, normalization by scaling was performed per variable. In summary, 13 variables were removed because these variables have missing values only and 20 variables were removed because they have high correlation with other variables. The final number of variables is 72 plus the scientific literacy score of PV1SCIE.

Machine learning modeling

Our approach to determining the key variables that identify Filipino students with poor performance in science used machine learning, aiming to come up with a computational model that relates the input variables to the target variables. The design for the computational model is evaluated in terms of training and test accuracy to measure the model performance in both seen and unseen data and the Area under the Region-of-Convergence curve (AUC) to determine how well the separation of data is in the model space.

Exhaustive hyperparameter search on the following computational models: support vector machines (SVM), logistic regression, multilayer perceptron (MLP), decision tree, and random forest (RF). Performance in terms of accuracy revealed that the best model is the RF classifier, having 500 estimators, maximum decision tree depth equal to 20, and maximum features equal to ceiling(log 2 71) = 7 variables per individual tree, which is the best classifier. (Please refer to Supplementary File for the performance summary of the machine learning models considered.) To illustrate the RF model, please refer to Fig. Fig.3. 3 . The RF model is composed of several independent decision trees that are trained independently on a random subset of data. To measure the quality of a split, entropy is used to measure the information gain.

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It is composed of n = 500 decision trees called estimators with a maximum tree depth of 20. Each input to the estimator uses only a subset of variables equal to ceil(log 2 71) or 7 variables. This minimizes the model overfitting due to the original large number of variables.

Model performance

The summary of the model performance is shown in Fig. Fig.4. 4 . The positive class for this study refers to the poor-performing class while the negative class refers to the better-performing class. Since the test dataset is not balanced, three performance metrics were observed: classification accuracy, precision, and recall. Accuracy is the ratio of correctly classified students, whether poor-performing or better-performing students, over the total number of students. Precision is defined as the ratio of the number of correctly predicted poor-performing students and the total number of predicted poor-performing students. Recall is the ratio of correctly predicted poor-performing students and the total number of poor performing. High precision and recall show that the model is returning accurate results (high precision), and returning a majority of all positive results (high recall).

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The confusion matrix ( a ) and the ROC ( b ) summarizing the performance of the RF model in classifying the PISA 2018 Science Proficiency of Filipino students. The average accuracy is 0.74 and the area under the ROC being equal to 0.83.

The RF Classifier returned a good balance of precision and recall on the training data with values equal to 0.74, and 0.79, respectively. In addition to this balance, among the different classifiers considered, the grid-search accuracy (see Fig. Fig.5), 5 ), shows that the RF classifier returned the best performance with final accuracy equal to 0.74, considering the precision and recall balance. The final precision, recall and accuracy using the test data are 0.73, 0.66, and 0.74, respectively. The area under the receiver operating curve (AUC) is 0.83 which implies a fairly good-fit model. A perfect classifier has AUC = 1.0 which implies that the model was able to separate the two classes, i.e. positive and negative, of data. The worst classifier, i.e. chance level accuracy, has AUC = 0.5.

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The scatterplot illustrating the range of test accuracies during the cross-validation on best machine learning models shows that the RF returned the best accuracies.

Model Interpretation

We investigated the feature importance learned during training by the RF classifier. We used Shapley additive explanations (SHAP) which is a scheme based on cooperative game theory to interpret the contributions of features in the prediction. For the RF classifier in this study, these top 15 key features or variables are shown in Fig. Fig.6 6 . 1 The important variables can positively affect or negatively affect the prediction of poor performance class ( y = 1). Particularly, one student with higher values for the variables BELONG, WORKMAST and BEINGBULLIED will negatively affect the prediction of identifying the poor performers in science. Similarly, for students with high ST164Q05IA, BSMJ, and HISEI values, the prediction of identifying poor performers in science is higher since these values positively impact this prediction. We describe the 15 variables in meaningful groupings below.

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Blue bars represent variables that negatively affect the prediction of poor-performing students while red bars indicate that a variable positively affects the prediction of poor-performing students.

The largest cluster of variables relates to students’ metacognitive awareness in reading or their perceptions about the usefulness of particular metacognitive strategies when reading texts in their classes. These variables are related items, where students were asked to indicate whether the indicated strategy is useful for understanding and memorizing the texts they read. Three of the variables positively identified the poor-performing students: (ST164Q05IA) “I summarize the text in my own words,” (ST164Q04IA) “I underline important parts of the text,” and (ST164Q02IA) “I quickly read through the text twice.” These three reading strategies involve relatively low metacognitive skills and are often ineffective, and poor-performing Filipino science students tend to see them as useful. On the other hand, two of the variables negatively identified poor-performing students: (ST164Q01IA) “I concentrate on the parts of the text that are easy to understand,” and (ST164Q03IA) “After reading the text, I discuss its content with other people.” The poor-performing Filipino science students tend to perceive these strategies as not useful.

The next largest cluster of variables relates to the student’s classroom and school experiences. Sense of belonging (BELONG) and perceived cooperation among students (PERCOOP) both negatively identify poor-performing students; that is, students who perform poorly in science report a low sense of belonging and perceive less cooperation among students. These two variables suggest negative social relations experienced by poor performers in science. Fortunately, self-report of experiencing bullying (BEINGBULLIED) was also negatively identified as the poor performers in science, so they tended to report less experiences of bullying in school. The last variable related to classroom experiences was how often “Students don’t listen to what the teacher says” (ST097Q01TA), which negatively identified the poor performers in science. The poor-performing science students were less likely to say that students often do not listen to the teacher. We should clarify that the item refers to teachers who use English in their classes, which refers to teachers in several subjects including science, mathematics, and English.

Three variables relate to the students’ affective or motivational experiences. The student’s motivation to master assigned learning tasks (WORKMAST) negatively identify poor-performing students, which means they tend to have low work mastery motivation. On the other hand, the student’s expected occupational status (BSMJ) and feeling proud about the things they accomplished (ST188Q02HA) both positively identified the poor-performing students. So the students who scored very low scores in science also tended to report higher job aspirations and being proud of their accomplishments compared to others. It seems that the student’s low achievement in science is unrelated to their future occupational plans and their present sense of accomplishment.

Finally, the remaining variables relate to the student’s family and home learning resources. Having smartphones with internet access at home (ST012Q05NA) negatively identified the poor-performing students, which means they were less likely to have this learning resource. But interestingly, the mother’s education (ST005Q01TA) negatively identified the poor-performing students, but the parents’ occupational status (HISEI) positively identified the students. This means that having mothers with lower educational attainment but having parents with high-status occupations also identified the students who were performing poorly in science. We could be seeing a pattern where low achievement in science is probably not viewed or experienced as a hindrance to higher-status professions. We explore this point and other results in the discussion section.

We used machine learning approaches to explore the best model for identifying the poorest-performing Filipino students in science using the PISA 2018 data. The Random Forest model was found to have the highest accuracy performance and the SHAP analysis indicated 15 variables that identified the poorest-performing science students.

Caveats and limitations

Before we discuss the meaning and implications of the details of the results, we need to underscore some important limitations in our study. First, our study cannot speak to the instructional and curricular factors that are typically the subject of discussions on improving science education in the Philippines. Second, the predictors in the model were limited to the variables in the PISA student self-report questionnaire. While there was a wide range of variables in the student questionnaire, many of the questions referred to reading (because the 2018 cycle of PISA was focused on reading), and thus, could not be included in our study. We also did not include variables from the school-head questionnaire about school characteristics, resources, and practices; nor could we include other potentially important predictors of science achievement that were not included in the PISA. Thus, there are possibly other variables that identify poor-performing students that are beyond the scope of this inquiry.

One important caveat relates to the predictive nature of the machine learning approaches, which treat variables equally without any theoretical presuppositions. Machine learning approaches focus on prediction accuracy and is not used to test explanatory models that specify theoretical relationships among variables (Shmueli, 2010 ; Yarkoni and Westfall, 2017 ). As such, the variables identified in the most accurate model may not have any obvious theoretical connection. These caveats notwithstanding, there are useful insights revealed by the analysis, which we discuss below.

Reading strategies for learning science

Metacognitive awareness regarding five different strategies identified the poor performers in science. It may seem surprising that reading strategies play an important role in identifying poor science performers, but the results make sense if one considers that much of science learning might be based on reading science textbooks (instead of doing laboratory experiments or field projects). Research with Italian students, for example, showed a difference in science achievement between good and bad readers, regardless of whether the science items involved low or high reading demand (Caponera et al., 2016 ). There were similar associations between reading comprehension and science achievement in a study of Spanish (Cano et al., 2014 ) and Filipino students (Imam et al., 2014 ). We note that our results do not actually involve reading comprehension, but metacognitive awareness of reading strategies, similar to a study of Croatian students that established a relationship between students’ reading strategies and comprehension of scientific texts (Kolić-Vrhovec et al., 2011 ). It is plausible that poor achievers in science are those that might be adopting the wrong reading strategies in reading their science textbooks.

Families’ and students’ resources and aspirations

High social, cultural, and economic resources in the students’ families (Lam and Zhou, 2021 ; Sun et al., 2012 ) and higher professional aspirations (Lee and Stankov, 2018 ) are typically associated with better achievement. But in our results, the poor-performing students were identified by higher job aspirations and stronger pride about one’s achievements. It is as if low achievement in science was not a consideration when students think about their future occupations nor when they assess their self-worth and pride. If we consider that the lower educational attainment of the mothers and higher occupational status of parents also predicted the poor-performing, it may be that students view their poor achievement in science as not relevant to their future occupational prospects, as their parents enjoy good occupations, even if their mothers are not highly educated. This interpretation asserts that science achievement might not be valued in pragmatic terms by the students based on what they see in their elders, which might also explain the role of low work mastery in school in identifying poor-performing students. Indeed, it is possible that many high-status occupations in the Philippines do not require knowledge of science, and as such, persevering and doing well in science might not be an important motivation among the students. This interpretation will need to be verified in future studies.

Negative social experiences

It was interesting to note that experience of bullying was a negative factor in the model, so it was not the case that experience of bullying was positively linked to poorer science achievement, as was found in Chinese students (Huang, 2020 ). However, two factors that indicate relational issues in school are identified with the poor performers: reporting a low sense of belonging and low cooperation among students in school. These factors suggest that a lack of connectedness and a collective spirit might be associated with poor science performance. Trinidad ( 2020 ) found that school-level and student-level measures of school climate were predictors of Filipino students’ mathematics achievement; such social factors might also have similar roles in Filipino learners’ poor science achievement.

Access to ICT for learning

One factor that may be increasingly important in identifying poor science achievers is access to ICT devices with internet access. Studies on Filipino students; PISA achievement in reading (Bernardo et al., 2021 ) and mathematics (Bernardo et al., 2022 ) also found the same factor as a predictor of achievement, consistent with much of the research in other countries (Hu et al., 2018 ; Petko et al., 2017 ; Yoon and Yun, 2023 ; but see Bulut and Cutumisu, 2018 ). Presumably, access to the internet outside the school environment has become an important resource for learning science; perhaps not just for accessing relevant scientific knowledge available online but also as a means of communicating with classmates for information sharing, collaboration in learning activities, and supporting each other’s motivations and engagement in science learning. Filipino students without such access are disadvantaged in the domain of science.

Practical implications: Focusing on the lowest achievers

The current study provides some analysis that could inform reform efforts in the domain of science learning, particularly as it concerns the lowest-achieving Filipino students in science. The results and discussion focus on factors that seem to characterize these lowest-achieving science students, and as such provide entry points to identifying these students and designing interventions for this particular group of students. Our approach focuses on the sizable proportion (over 35%) of Filipino students who have been assessed as demonstrating extremely low competencies (levels 1b and below) in science. The Philippine educational system does not lack programs for the more gifted students in science such as special science schools (Faustino and Hiwatig, 2012 ), competitions, scholarships, and other forms of support for students pursuing advanced studies and careers in science (De La Cruz, 2022 ). But there is not much that is documented about what is being done for the students like the 35% who are demonstrating extremely low levels of scientific literacy, even if they have progressed to the high school levels of the country’s formal education system. The first important implication of our findings is that these students need to be identified and understood before their science learnings can be addressed.

We should clarify that the characterization of poor-performing Filipino students in science should not be interpreted as the opposite characterization of better- or high-performing students. It is likely that there are qualitative differences between the experiences of poor and better science learners that are not captured by simply assuming a linear relationship between the factors that predict science learning. Indeed, if our machine learning approach was applied to identify the high-achieving students (i.e., Levels 4–6), it is likely that a different set of variables will be in the best machine learning model (and that can be explored in a different study). But by implication, the characterization of the poor-performing students in the results does not point to simple instructional or curricular interventions, and we do think there are some important policy implications that can be considered by stakeholders who are concerned with improving science education achievement among Filipino learners.

Instructional programs for poor achievers

Science educators have long noted that there are profound diversities in students of different ability levels, that simply assuming that one form of good teaching fits all types of learners is no longer tenable (Ault, 2010 ; Lynch, 2001 ; Yang et al., 2019 ). In this regard, the science education reform community of stakeholders should consider moving away from a one-size-teaching-fits-all approach that tends to be designed for students in the middle range of abilities using whole class instruction, and instead, move towards approaches that consider diverse adaptive learning approaches (Yang et al., 2019 ) and differentiated instruction (Pablico et al., 2017 ) that might be more responsive to (or at least that might not simply ignore) the needs of the low achievers.

Ensuring reading skills

There is a lot of evidence that good reading strategies and reading comprehension are strongly associated with science achievement (Cano et al., 2014 ; Caponera et al., 2016 ; Kolić-Vrhovec et al., 2011 ), but Filipino learners on average have extremely poor reading skills in English (Bernardo et al., 2021 ), which is the medium-of-instruction in science. Presumably, there are science learning activities that are more experiential and discovery-oriented and less dependent on students’ reading textbooks; but a previous study of students’ perception of science classes revealed a trend of decreasing science inquiry activities accompanied by an increase in self-learning, presumably involving reading textbooks and learning modules from Grade 5 to 10 (Bernardo et al., 2008 ). If Filipino science learners will be expected to do much of their learning through textbooks and learning modules in English, there should be strong efforts to strengthen the reading strategies and skills of Filipino learners.

Science in future professions and Philippine society

We interpreted part of the results as being associated with the view that science learning and achievement are irrelevant to higher future occupational aspirations. While these interpretations are speculative, there is probably a strong basis for the view that one does not need science to attain respectable occupations in the Philippines. There are many models of successful Filipino professionals and individuals who do not seem overtly display knowledge and use of science. In this regard, efforts to improve the science achievement of Filipino students might need to reckon with the perceived irrelevance of science in Philippine society. Scholars have problematized the lack of a science culture in the Philippines (Pertierra, 2004 ), perhaps vividly displayed in the recent COVID-19 pandemic, when there was widespread uncritical sharing of misinformation on vaccines, false cures, and other scientific matters through social media and social networks (Amit et al., 2022 ) and when scientific advice on pertinent issues was diluted and filtered before decisions were made by national leaders (Vallejo and Ong, 2020 ). Beyond schools, there should be efforts to change public perceptions of the importance of science in Filipinos’ social mobility and Filipino society’s development.

Improving school climate

The poor-performing students in science were identified by reports of a low sense of belonging in school and low perceived cooperation among students. These social experiences may be associated with lower achievement as they indicate a lack of meaningful sense of connectedness with students and teachers in the school, which is associated with lower engagement in the science classes, even if the social experiences are not specifically confined or referring to the science classes. The factors that contribute to these negative social experiences might vary across schools and communities and should be understood in proper contexts. Once the nature and causes of these social experiences are better understood, appropriate contextualized interventions can be developed.

Access to ICT devices and connectivity

Previous studies have documented how ICT availability and use positively predicted student achievement (Hu et al., 2018 ; Petko et al., 2017 ; Yoon and Yun, 2023 ), and similar results were also found in Filipino students’ achievement in reading (Bernardo et al., 2021 ), mathematics (Bernardo et al., 2022 ), and now in science. Together with improving access to the internet, there should be an effort to train teachers and students how to more effectively use the internet to deepen their learning of science concepts and processes, and in ways that adapt to students’ diverse abilities, interests, motivations, and circumstances (Yang et al., 2019 ).

Conclusions

Based on the assumption that science-for-all requires all Filipino citizens to acquire the scientific literacy required to effectively engage with and contribute to Philippine society in the 21st Century, we focused on the Filipino students with the lowest levels of science achievement in PISA 2018. We used machine learning to explore the variables that best identify the poor-performing Filipino students, as these variables could be used to better track and understand their learning needs. Our study points to a cluster of variables related to the student’s reading strategies, occupational aspirations, social experiences in school, and access to ITC and the internet. The variables depart from the typical focus of reform efforts on teachers’ competencies, curriculum, and instruction. But if we truly want to improve Filipino students’ science literacy, we need to understand the experiences of students who are failing to do so, as these point to problems that need to be addressed in their learning experiences in Philippine schools.

Supplementary information

Acknowledgements.

This research was funded by a grant to the third author from the De La Salle University-Angelo King Institute for Economic and Business Studies (AKI Research Grants 2020–2021 Project No. 500-139), and a Research Fellowship to the first author from the National Academy of Science and Technology, Philippines.

Author contributions

Conceptualization: ABIB, MOC II, MOC; Machine learning methodology, MOC II; Machine learning modeling and evaluation: JMMT; Data preprocessing and feature engineering: SAY; Data visualization: UCC; Writing—original draft preparation, review, and editing: ABIB, MOC II, MOC; Project administration: MOC II, MOC; Funding acquisition: MOC, MOC II, ABIB.

Data availability

Competing interests.

The authors declare no competing interests.

Ethics approval

The study involved secondary analyses of the officially published PISA 2018 dataset; as such ethics review and approval and informed consent does not apply. This dataset was downloaded as a public use file from the OECD website ( https://www.oecd.org/pisa/data/2018database/ , accessed 17 Feb 2020).

Informed consent

This paper involved no primary data collection on the part of the authors. However, PISA 2018 data collection procedures indicate that parents of the students who participated in the assessment provided informed consent.

1 For completeness, we also conducted a SHAP analysis for the best algorithm for each of the other machine learning approaches. A comparative summary of the top 15 variables that feature in the prediction models is shown in Supplementary File.

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

The online version contains supplementary material available at 10.1057/s41599-023-01705-y.

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Clinical laboratory regulation in the Philippines: Are we doing it right? (Part I)

UNDER THE MICRCOSCOPE

I’d like to share my observations on the clinical laboratory regulations in the Philippines with the reprinting of my article which was published in the Philippine Journal of Pathology (Vol.6 No.2 December 2021), as this gives a background on the challenges faced by Clinical Pathology practitioners:

I write this article from my perspective of 34 years of practice in the Philippines having arrived in 1987 from my training in the United States of America. In my six years of training first in Anatomic and Clinical Pathology then in Immunopathology fellowship, I have witnessed the workings of laboratory regulation and accreditation in that country and was in culture shock when I started my practice here. Laboratory automation was well underway in the Western hemisphere during my training. Hematology and chemistry at the forefront of this technological revolution. But other disciplines were starting to catch up. Locally, few tertiary labs were starting to automate. I was, of course, excited to begin revolutionizing the practice of Clinical Pathology.

I had not counted on the backward state of laboratory regulation locally. Under the Department of Health regulatory office, Bureau of Research and Laboratories (BRL), inspection visits to the tertiary labs that I was connected with (one private and one government hospital lab), the inspectors were engineers who came with a list of requirements that included asking for Benedict’s reagent (for blood sugar manual testing) even though we were already using automated chemistry methods. This stopped only 10 years ago, thankfully when the BRL was renamed Bureau of Health Facilities Services (BHFS), now the Health Facilities Services Regulatory Bureau (HFSRB).

The other issue is that there tends to be over regulation. Elsewhere, the emphasis is on accurate laboratory test results which is achieved through good quality assurance programs and performing satisfactorily on semi-annual proficiency testing. On-site inspections are geared towards these goals as well as the right physical set-up necessary to achieve these results. Our local regulatory agency tends to aim at including training programs which normally are the province of professional societies.

An example is HIV testing. When it was first proposed, you have to apply for a separate HIV testing license apart from the license to operate a clinical laboratory. To get the license, a medical technologist must have to attend and pass an HIV Proficiency training course which includes counselling patients. The course was being given by Research Institute of Tropical Medicine (RITM) on a limited basis, thus, only few medical technologists were licensed, severely limiting the number of labs that can perform HIV testing.

I had argued against these regulations. The HIV antibody (and subsequently the HIV antigen test) test was a routine serologic procedure that can be done on a manual or automated method either by EIA or ECLIA. To run these tests, one needs only to be conversant with the requirements for running the samples like any other serologic examination.

Secondly, the ruling ignores the role of the pathologist who is responsible for the test result ultimately. Third, the medical technologist is not the best person to be counselling patients. It is the attending physician who orders the test. Putting the burden of counselling on the med tech is a disservice to the patient and takes the med tech away from his/her main job, which is to run the tests.

We now have a situation where the regulatory agency prescribes training which is often unavailable but labs are expected to comply. The offshoot is that the few medical technologists who had the training became highly sought after to be able to put up HIV testing in clinical labs, leading to a black market where these techs offer their licenses for a fee. (Addendum: The HIV training requirement was recently removed.)

The requirement for pre- and post-test counselling actually deterred patients from testing since it attaches a stigma to the disease which was what the regulation was supposed to remove. In other countries, one can simply walk into a lab and ask for the HIV test without any additional requirements. Some even offer anonymous testing.

All these factors — limited testing, counselling requirements and the attendant stigma attached may have led to the explosive increase in HIV cases we are seeing now in the Philippines.

Fast forward to today and not much has changed. With the SARS-CoV-2 pandemic, molecular laboratories for RT-PCR testing of the virus had to be set up quickly to enable adequate testing. Regulations governing the physical set up described a sample laboratory design with instructions on one way flow. It was not meant to be the template but when we submitted our design following the instructions, it was not allowed. No matter how we modified it, it all boiled down to just follow the sample design, no matter if the space configuration did not allow for it. That to and fro took all of one month, precious time wasted at the height of the pandemic.

(To be continued)

HealthTech in the Philippines

A smiling male-presenting person of Asian heritage with black hair wearing a black suit and tie against a wooden background

Quintus Lim

The COVID-19 pandemic has been a stark warning to the Philippines and the rest of the world: Good health is critical to economic and societal functioning, and countries that neglected their health sectors have reaped the consequences. At the same time, societal, medical, and regulatory attitudes toward health innovations such as telemedicine were upended, and the possibilities seemed limitless, if only for a few years. As priorities in 2024 turn toward economic recovery, it falls on health systems to retain their learnings from the pandemic and invest more heavily in technologies to strengthen health and care in the long term.

In the Philippines, matters of health care are often tethered inextricably to local politics. Concurrently, the humility and candidness of public discourse can be advantageous. Politicians, academia, and nongovernmental organizations alike are explicit on what needs to improve, and even government reports can be unusually blunt about their own performance. This clear-eyed view has partly translated into a steady improvement in health system capacity over the years.

Barangay (district) health stations have proliferated since 2004, and under-five mortality has halved since 1993. Sin taxes bring in revenue larger than the entire health budget, providing much-needed funding for UHC. Malaria has been suppressed, and additional laws on reproductive health, cancer, occupational safety, mental health, and integrated health services and financing have been passed.

In particular, the 2019 UHC Act marked a major milestone in equalizing access to health care. From a meager 38 percent coverage in 2000, all Filipinos will now be automatically enrolled, with health care increasingly delivered at the community level, focused on prevention, and integrated with the rest of the health system. The Department of Health (DOH) aims for Filipinos to be “among the healthiest people in Asia by 2040.”

Despite these advances, lifespans in the Philippines have risen relatively slowly since 1990 (Figure 1), and widespread stunting of children’s growth will continue to slow future advances in healthy life expectancy. This is not entirely an issue of income or geography—poorer countries such as India and larger archipelagoes such as Indonesia have lengthened lifespans more despite spending less. Such sobering statistics represent the unfinished work in Philippines health and care.

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Research Centers
Evelyn D. Ang – Institute of Biomedical Engineering and Health Technologies (EDA-IBEHT)

Background and History

Bachelor of Science in Manufacturing Engineering and Management (BS MEM) Program was established in School Year 1986-87 after a thorough consultation with the Government and Industry.

Patterned from Loughborough University in England, United Kingdom.

First batch with 45 students graduated in 1991 .

MEM – a multidisciplinary program split into 2 specializations in SY 2003-2004. MEM with specialization in Mechatronics and Robotics Engineering (MEM-MRE). MEM with specialization in Biomedical Engineering (MEM-BME).

In 2005, the Manufacturing Engineering and Management (MEM) Department of the College of Engineering (COE) – De La Salle University (DLSU) was awarded the EU-ASIA Link Project through the Leaderships of Dr. Julius Maridable (Dean, COE) and Dr. Nilo Bugtai (Chair, MEM) .

Project Title: Development of Core Competencies in the Area of Biomedical and Clinical Engineering in the Philippines and Indonesia

April 2005 – March 2007 Budget: PhP 26M

International Partners:

University of Indonesia Royal University of Sweden University of Pisa, Italy

Proposal for the creation of a new RESEARCH CENTER at DLSU to the Vice-Chancellor for Research & Innovation

PRESENTED to the following councils for approval:

Center Director’s Forum University Research Council University Academic Council Chancellor’s Council President’s Council

APPROVED on July 2019 IMPLEMENTED on September 2019

What is IBEHT?

A One-Stop-Shop for information and support infrastructure on biomedical devices/equipment & health technologies research in the Philippines.
A Lab-to-Market Roadmap for R&D projects under the research areas of the Philippine Harmonized National R&D Agenda in Health, particularly in the National Unified Health Research Agenda (NUHRA). It strengthens biomedical devices/equipment & health technologies researchers through sharing of ideas, experience, and technical expertise.
A Go-to-Centers for researchers, scientists, engineers, clinicians, industry practitioners and innovators that focus on biomedical devices and hospital equipment researches. It will include thematic research centers in the future such as for Robotic Assistive & Rehabilitation Devices; Robotic Surgical Devices & Technologies; Biomedical Devices & Technologies for Animals; Neurorobotics Technology Center, IBEHT Commercialization Hub and Calibration and Safety Testing Center. It promotes collaborative, multi-department, interdisciplinary research projects as well as joint proposals for funding from external sources.
A Venue for the application and initial screening process of the foreign MSc and PhD Scholarships on Biomedical Engineering degree program supported and funded by DOST-SEI. It enables the community with capacity building activities such as trainings, workshops, seminars, and other similar events.

BME Program Offering

DE LA SALLE UNIVERSITY

Gokongwei College of Engineering Manufacturing Engineering and Management (MEM) Department

BS MEM with specialization in Biomedical Engineering (BS MEM-BME) Started in School Year 2003-2004
Bachelor of Science in Biomedical Engineering (BS BME)
Master of Science in Biomedical Engineering (MS BME) Starts in Academic Year 2021-2022

BATANGAS STATE UNIVERSITY

College of Engineering

Bachelor of Science in Biomedical Engineering Starts in School Year 2020-2021

ATENEO DE ZAMBOANGA UNIVERSITY

College of Science

Bachelor of Science in Biomedical Engineering Starts in School Year 2017-2018

MAPUA UNIVERSITY MANILA

School of Chemical, Biological and Materials Engineering and Science

Bachelor of Science in Biomedical Engineering and Chemistry Starts in School Year 2020-2021

From Biomedical Devices Innovation & e-Health (BDI) Research Group to IBEHT

AGAPAY Project is a wearable robotic exoskeleton that is biomimetically designed to account for all the movements of the upper extremity. It shall provide post-stroke and injured patients with high performance rehabilitation system that is reliable, easy to operate, safe to use and affordable.

Initially proposed for the grant under PCARI , later to the PCHRD by

Dr. Nilo Bugtai, PhD Engr. Rennan Baldovino, MSc Paul Dominick Baniqued

BDI: AGAPAY Project – PHASE 1

PROGRAM Proposal for the Establishment of a National Institute of Biomedical Engineering and Health Technologies

SUBMITTED to DOST – PCHRD

APPROVED on September 18, 2018 during the DOST-PCHRD GC Meeting

IMPLEMENTED on March 19, 2019

Mission – Vision

Our mission is to pave way in accelerating the development of biomedical devices and technologies by providing state-of-the-art facilities and services to research universities and institutions; enabling them to transform their ideas into market-ready products.

By 2025, DLSU – IBEHT will be:

At the forefront of biomedical devices innovation and research in the Philippines, producing ground-breaking technologies in healthcare and related areas.
Recognized nationally as the go-to-centers for scientists, engineers and innovators focusing on research for biomedical devices and technologies in the country.

IBEHT Advisory Council

Vice Chancellor for Research

De La Salle Medical and Health Sciences Institute

Dasmarinas Cavite

Chancellor

University of the Philippines – Manila

Co-Founder, VP for Technology Development & IP

Xylos Corporation, Langhorne PA, USA

Co-Founder & Vice President for Operations

Plug & Play Tech Center, Silicon Valley USA

Senior Vice President

United Laboratories (Unilab), Philippines

Vice President for External Relations & Internalization

De La Salle University – Manila

Former Director, Documentation, Information, and Technology Transfer Bureau

Intellectual Property Office of the Philippines (POPHL)

Former Chief, Research Information, Communication and Utilization Division

PCHRD – DOST

Former Vice Chancellor for Academics & Research