Journal of Educational Evaluation for Health Professions

Research article

Personality type profiles of medical students and their differences by gender, age, and academic level in Korea: a cross-sectional study: Yera Hur, Sanghee Yeo; J Educ Eval Health Prof. 2026;23:7. Published online April 28, 2026; DOI: https://doi.org/10.3352/jeehp.2026.23.7

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Abstract PDF Supplementary Material: Purpose
Understanding the psychological characteristics of contemporary medical students is essential for effective educational design and learner support. This study aimed to identify medical students’ personality types using a geometric personality assessment tool (GEOPIA), determine whether differences exist by gender, age, or academic level, and explore the practical utility of such profiling for supporting educational practices in medical school settings.
Methods
The 40-item Korean Geometric Psychological Assessment (GEOPIA) was administered to 1,173 students across 5 Korean medical schools. GEOPIA classifies individuals into 4 primary types—Round (sociable, relationship-oriented), Triangle (task-oriented, challenging), Box (prudent, stability-seeking), and Curve (creative, sensitive). Frequency analyses and χ² tests were conducted. Of the 1,016 respondents (response rate, 86.61%), 981 were included in the final analysis.
Results
The most common primary type was Round (40.3%), followed by Box (31.7%), Triangle (15.2%), and Curve (12.8%). Across the 12 combined profiles, Round–Box (21.9%) was the most prevalent, followed by Box–Round (19.0%) and Round–Triangle (9.7%). No significant differences were observed by gender (χ²=6.360, P=0.095, Cramer’s V=0.082), age (χ²=11.454, P=0.490, Cramer’s V=0.065), or academic level (χ²=18.044, P=0.260, Cramer’s V=0.078).
Conclusion
GEOPIA may provide a practical tool for identifying learner characteristics and supporting educational decision-making in medical school settings. In instructional design, personality-type data can inform group formation, activity planning, and assignment structure. In student support, the tool offers instructors and advisors a quick way to understand learners’ characteristics, which may help guide individualized counseling and promote effective learning experiences.

Review

The impact of artificial intelligence-driven simulation on the development of non-technical skills in medical education: a systematic review: Sana Loubbairi, Yasmine El Moussaoui, Laila Lahlou, Imad Chakri, Hicham Nassik; J Educ Eval Health Prof. 2025;22:37. Published online November 24, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.37

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Purpose
Artificial intelligence (AI)-driven simulation is an emerging approach in healthcare education that enhances learning effectiveness. This review examined its impact on the development of non-technical skills among medical learners.
Methods
Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic review was conducted using the following databases: Web of Science, ScienceDirect, Scopus, and PubMed. The quality of the included studies was assessed using the Mixed Methods Appraisal Tool. The protocol was previously registered in PROSPERO (CRD420251038024).
Results
Of the 1,442 studies identified in the initial search, 20 met the inclusion criteria, involving 2,535 participants. The simulators varied considerably, ranging from platforms built on symbolic AI methods to social robots powered by computational AI. Among the 15 AI-driven simulators, 10 used ChatGPT or its variants as virtual patients. Several studies evaluated multiple non-technical skills simultaneously. Communication and clinical reasoning were the most frequently assessed skills, appearing in 12 and 6 studies, respectively, which generally reported positive outcomes. Improvements were also noted in decision-making, empathy, self-confidence, critical thinking, and problem-solving. In contrast, emotional regulation, assessed in a single study, showed no significant difference. Notably, none of the studies examined reflection, reflective practice, teamwork, or leadership.
Conclusion
AI-driven simulation shows substantial potential for enhancing non-technical skills in medical education, particularly communication and clinical reasoning. However, its effects on several other non-technical skills remain unclear. Given heterogeneity in study designs and outcome measures, these findings should be interpreted cautiously. These considerations highlight the need for further research to support integrating this innovative approach into medical curricula.

Citations

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The Effect of Infection Control Session on Nursing Students' Knowledge and Compliance with Standard Precautions at Hassan College of Nursing, Swat
Ahmad Ullah, Hammad Ullah Khan, Zia Ullah Khan, Numan Khan, Shah Hussain
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Empowering Surgical Training through Artificial Intelligence: A Cross-Sectional Study on Residents’ Acceptance and Perceived Usefulness of AI-Based Simulation
Sami Ur Rahman, Kulsoom Nadir, Muhammad Ilyas, Mehar Nigar, Anwar Khan, Abdur Rahman, Shah Hussain
medtigo Journal of Medicine.2026;[Epub] CrossRef

Technical report

Feasibility of applying computerized adaptive testing to the Clinical Medical Science Comprehensive Examination in Korea: a psychometric study: Jeongwook Choi, Sung-Soo Jung, Eun Kwang Choi, Kyung Sik Kim, Dong Gi Seo; J Educ Eval Health Prof. 2025;22:29. Published online October 1, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.29

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Abstract PDF Supplementary Material: Purpose
This study aimed to investigate the feasibility of transitioning the Clinical Medical Science Comprehensive Examination (CMSCE) to computerized adaptive testing (CAT) in Korea, thereby providing greater opportunities for medical students to accurately compare their clinical competencies with peers nationwide and to monitor their own progress.
Methods
A medical self-assessment using CAT was conducted from March to June 2023, involving 1,541 medical students who volunteered from 40 medical colleges in Korea. An item bank consisting of 1,145 items from previously administered CMSCE examinations (2019–2021) hosted by the Medical Education Assessment Corporation was established. Items were selected through 2-stage filtering, based on classical test theory (discrimination index above 0.15) and item response theory (discrimination parameter estimates above 0.6 and difficulty parameter estimates between –5 and +5). Maximum Fisher information was employed as the item selection method, and maximum likelihood estimation was used for ability estimation.
Results
The CAT was successfully administered without significant issues. The stopping rule was set at a standard error of measurement of 0.25, with a maximum of 50 items for ability estimation. The mean ability score was 0.55, with an average of 28 items administered per student. Students at extreme ability levels reached the maximum of 50 items due to the limited availability of items at appropriate difficulty levels.
Conclusion
The medical self-assessment CAT, the first of its kind in Korea, was successfully implemented nationwide without significant problems. These results indicate strong potential for expanding the use of CAT in medical education assessments.

Research articles

Performance of ChatGPT-4 on the French Board of Plastic Reconstructive and Aesthetic Surgery written exam: a descriptive study: Emma Dejean-Bouyer, Anoujat Kanlagna, François Thuau, Pierre Perrot, Ugo Lancien; J Educ Eval Health Prof. 2025;22:27. Published online September 30, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.27

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Abstract PDF Supplementary Material: Purpose
This study aims to evaluate the performance of Chat Generative Pre-Trained Transformer 4 (ChatGPT-4) on the French Board of Plastic, Reconstructive, and Aesthetic Surgery written examination and to assess its role as a supplementary resource in helping residents prepare for the qualification examination in plastic surgery.
Methods
This descriptive study evaluated ChatGPT-4’s performance on 213 items from the October 2024 French Board of Plastic, Reconstructive, and Aesthetic Surgery written examination. Responses were assessed for accuracy, logical reasoning, internal and external information use, and were categorized for fallacies by independent reviewers. Statistical analyses included chi-square tests and Fisher’s exact test for significance.
Results
ChatGPT-4 answered all questions across the 10 modules, achieving an overall accuracy rate of 77.5%. The model applied logical reasoning in 98.1% of the questions, utilized internal information in 94.4%, and incorporated external information in 91.1%.
Conclusion
ChatGPT-4 performs satisfactorily on the French Board of Plastic, Reconstructive, and Aesthetic Surgery written examination. Its accuracy met the minimum passing standards for the exam. While responses generally align with expected knowledge, careful verification remains necessary, particularly for questions involving image interpretation. As artificial intelligence continues to evolve, ChatGPT-4 is expected to become an increasingly reliable tool for medical education. At present, it remains a valuable resource for assisting plastic surgery residents in their training.

Correlation between task-based checklists and global rating scores in undergraduate objective structured clinical examinations in Saudi Arabia: a 1-year comparative study: Uzma Khan, Yasir Naseem Khan; J Educ Eval Health Prof. 2025;22:19. Published online June 19, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.19

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Abstract PDF Supplementary Material

Purpose
This study investigated the correlation between task-based checklist scores and global rating scores (GRS) in objective structured clinical examinations (OSCEs) for fourth-year undergraduate medical students and aimed to determine whether both methods can be reliably used in a standard setting.
Methods
A comparative observational study was conducted at Al Rayan College of Medicine, Saudi Arabia, involving 93 fourth-year students during the 2023–2024 academic year. OSCEs from 2 General Practice courses were analyzed, each comprising 10 stations assessing clinical competencies. Students were scored using both task-specific checklists and holistic 5-point GRS. Reliability was evaluated using Cronbach’s α, and the relationship between the 2 scoring methods was assessed using the coefficient of determination (R2). Ethical approval and informed consent were obtained.
Results
The mean OSCE score was 76.7 in Course 1 (Cronbach’s α=0.85) and 73.0 in Course 2 (Cronbach’s α=0.81). R2 values varied by station and competency. Strong correlations were observed in procedural and management skills (R2 up to 0.87), while weaker correlations appeared in history-taking stations (R2 as low as 0.35). The variability across stations highlighted the context-dependence of alignment between checklist and GRS methods.
Conclusion
Both checklists and GRS exhibit reliable psychometric properties. Their combined use improves validity in OSCE scoring, but station-specific application is recommended. Checklists may anchor pass/fail decisions, while GRS may assist in assessing borderline performance. This hybrid model increases fairness and reflects clinical authenticity in competency-based assessment.

Citations

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The effectiveness of the “Early clinical exposure” course based on narrative medicine in cultivating the professional qualities of undergraduates in clinical medicine: a mixed-methods study
Zhao Li, Huijuan Cai, Xiaolin Yang, Jingsong Lin, Wenhua Cao, Peng Zhang, Jing Ren, Dayong Zheng
BMC Medical Education.2026;[Epub] CrossRef
Agreement and reliability of global rating versus checklist scores in a high-stakes undergraduate OSCE in Rwanda
Olayinka Rasheed Ibrahim, Natalie McCall, Abebe Bekele, Biniam Ewnte Zelelew, Oluwaseun Ojomo, Anteneh Gadisa Belachew, Equlinet Misganaw Amare, Zelalem Mengistu Gashaw, Birhanu Abera Ayana, Ariane Nina Ndayikeje
BMC Medical Education.2026;[Epub] CrossRef

Mixed reality versus manikins in basic life support simulation-based training for medical students in France: the mixed reality non-inferiority randomized controlled trial: Sofia Barlocco De La Vega, Evelyne Guerif-Dubreucq, Jebrane Bouaoud, Myriam Awad, Léonard Mathon, Agathe Beauvais, Thomas Olivier, Pierre-Clément Thiébaud, Anne-Laure Philippon; J Educ Eval Health Prof. 2025;22:15. Published online May 12, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.15

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Abstract PDF Supplementary Material

Purpose
To compare the effectiveness of mixed reality with traditional manikin-based simulation in basic life support (BLS) training, testing the hypothesis that mixed reality is non-inferior to manikin-based simulation.
Methods
A non-inferiority randomized controlled trial was conducted. Third-year medical students were randomized into 2 groups. The mixed reality group received 32 minutes of individual training using a virtual reality headset and a torso for chest compressions (CC). The manikin group participated in 2 hours of group training consisting of theoretical and practical sessions using a low-fidelity manikin. The primary outcome was the overall BLS performance score, assessed at 1 month through a standardized BLS scenario using a 10-item assessment scale. The quality of CC, student satisfaction, and confidence levels were secondary outcomes and assessed through superiority analyses.
Results
Data from 155 participants were analyzed, with 84 in the mixed reality group and 71 in the manikin group. The mean overall BLS performance score was 6.4 (mixed reality) vs. 6.5 (manikin), (mean difference, –0.1; 95% confidence interval [CI], –0.45 to +∞). CC depth was greater in the manikin group (50.3 mm vs. 46.6 mm; mean difference, –3.7 mm; 95% CI, –6.5 to –0.9), with 61.2% achieving optimal depth compared to 43.8% in the mixed reality group (mean difference, 17.4%; 95% CI, –29.3 to –5.5). Satisfaction was higher in the mixed reality group (4.9/5 vs. 4.7/5 in the manikin group; difference, 0.2; 95% CI, 0.07 to 0.33), as was confidence in performing BLS (3.9/5 vs. 3.6/5; difference, 0.3; 95% CI, 0.11 to 0.58). No other significant differences were observed for secondary outcomes.
Conclusion
Mixed reality is non-inferior to manikin simulation in terms of overall BLS performance score assessed at 1 month.

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Enhancing virtual reality applications for adult basic life support: insights from a comparative analysis
Nino Fijačko, Benjamin S. Abella, Vinay M. Nadkarni, Špela Metličar, Anne-Astrid Agten, Robert Greif
Virtual Reality.2026;[Epub] CrossRef
Enhancing STEM and STEAM Education at the Grade Level Through Mixed Reality Applications: A Meta-analytical Study
Segun Michael Ojetunde, Umesh Ramnarain
Canadian Journal of Science, Mathematics and Technology Education.2026;[Epub] CrossRef
IMPACTOS DO USO DE SIMULAÇÃO IMERSIVA NA CAPACITAÇÃO EM ATENDIMENTO À PARADA CARDIORRESPIRATÓRIA
Iago Brenner Farias Leal, Izabelly Ferreira de Andrade, Yan Carlos de Sousa Diniz, Lara Maria Ferreira Lopes Valéri Pinto, Maria Helena Vieira Pereira Marques, Francisca Evelyn Abreu de Lira, Thaís Helena Gomes de Sousa, Maria Isabelly Araújo Ferreira, A
Revista Multidisciplinar do Nordeste Mineiro.2025; 16(1): 1. CrossRef

Evaluation of a virtual objective structured clinical examination in the metaverse (Second Life) to assess the clinical skills in emergency radiology of medical students in Spain: a cross-sectional study: Alba Virtudes Perez-Baena, Teodoro Rudolphi-Solero, Rocio Lorenzo-Alvarez, Dolores Dominguez-Pinos, Miguel Jose Ruiz-Gomez, Francisco Sendra-Portero; J Educ Eval Health Prof. 2025;22:12. Published online April 21, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.12

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Purpose
The objective structured clinical examination (OSCE) is an effective but resource-intensive tool for assessing clinical competence. This study hypothesized that implementing a virtual OSCE in the Second Life (SL) platform in the metaverse as a cost-effective alternative will effectively assess and enhance clinical skills in emergency radiology while being feasible and well-received. The aim was to evaluate a virtual radiology OSCE in SL as a formative assessment, focusing on feasibility, educational impact, and students’ perceptions.
Methods
Two virtual 6-station OSCE rooms dedicated to emergency radiology were developed in SL. Sixth-year medical students completed the OSCE during a 1-hour session in 2022–2023, followed by feedback including a correction checklist, individual scores, and group comparisons. Students completed a questionnaire with Likert-scale questions, a 10-point rating, and open-ended comments. Quantitative data were analyzed using the Student t-test and the Mann-Whitney U test, and qualitative data through thematic analysis.
Results
In total, 163 students participated, achieving mean scores of 5.1±1.4 and 4.9±1.3 (out of 10) in the 2 virtual OSCE rooms, respectively (P=0.287). One hundred seventeen students evaluated the OSCE, praising the teaching staff (9.3±1.0), project organization (8.8±1.2), OSCE environment (8.7±1.5), training usefulness (8.6±1.5), and formative self-assessment (8.5±1.4). Likert-scale questions and students’ open-ended comments highlighted the virtual environment’s attractiveness, case selection, self-evaluation usefulness, project excellence, and training impact. Technical difficulties were reported by 13 students (8%).
Conclusion
This study demonstrated the feasibility of incorporating formative OSCEs in SL as a useful teaching tool for undergraduate radiology education, which was cost-effective and highly valued by students.

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Effectiveness of VR and traditional training in medical education for mass casualty management: an OSCE-based randomized controlled trial
Zhe Li, Wan Chen, Guozheng Qiu, Lei Shi, Yutao Tang, Xibin Xu, Sanshan Zhu, Liwen Lyu
BMC Medical Education.2026;[Epub] CrossRef
ECOE virtual de radiología en el metaverso Second Life®: comparación de estudiantes de tercer y sexto curso
Francisco Sendra-Portero, Alba Virtudes Pérez-Baena, Teodoro Rudolphi-Solero, Rocío Lorenzo-Álvarez, Dolores Domínguez-Pinos, Miguel José Ruiz-Gómez
Educación Médica.2026; 27(3): 101167. CrossRef
A roadmap to implement Metaverse in education field based on ADDIE model: A systematic review
Yu Shi, Wei Wei Goh, N.Z. Jhanjhi
Computers and Education Open.2026; 10: 100365. CrossRef
Metaverse-based objective structured clinical examinations: an exploratory approach to advancing clinical competency assessment
Yeon-Ju Huh, Joon Sung Shin, Narae Yoon, Ju Whi Kim, Do Hoon Kim, Chanwoong Kim, Seoi Jeong, Yejin Yoon, Soyeon Shin, Hyoun-Joong Kong, Sun Jung Myung
Korean Journal of Medical Education.2026; 38(2): 139. CrossRef

Empathy and tolerance of ambiguity in medical students and doctors participating in art-based observational training at the Rijksmuseum in Amsterdam, the Netherlands: a before-and-after study: Stella Anna Bult, Thomas van Gulik; J Educ Eval Health Prof. 2025;22:3. Published online January 14, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.3

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Abstract PDF Supplementary Material

Purpose
This research presents an experimental study using validated questionnaires to quantitatively assess the outcomes of art-based observational training in medical students, residents, and specialists. The study tested the hypothesis that art-based observational training would lead to measurable effects on judgement skills (tolerance of ambiguity) and empathy in medical students and doctors.
Methods
An experimental cohort study with pre- and post-intervention assessments was conducted using validated questionnaires and qualitative evaluation forms to examine the outcomes of art-based observational training in medical students and doctors. Between December 2023 and June 2024, 15 art courses were conducted in the Rijksmuseum in Amsterdam. Participants were assessed on empathy using the Jefferson Scale of Empathy (JSE) and tolerance of ambiguity using the Tolerance of Ambiguity in Medical Students and Doctors (TAMSAD) scale.
Results
In total, 91 participants were included; 29 participants completed the JSE and 62 completed the TAMSAD scales. The results showed statistically significant post-test increases for mean JSE and TAMSAD scores (3.71 points for the JSE, ranging from 20 to 140, and 1.86 points for the TAMSAD, ranging from 0 to 100). The qualitative findings were predominantly positive.
Conclusion
The results suggest that incorporating art-based observational training in medical education improves empathy and tolerance of ambiguity. This study highlights the importance of art-based observational training in medical education in the professional development of medical students and doctors.

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Understanding uncertainty and ambiguity in medicine and medical education: a narrative review with implications for training
Sarine Sarkis, Christian Raphael
Postgraduate Medical Journal.2026; 102(1207): 461. CrossRef
Observational training for surgical residents using visual arts in the museum
Thomas M. van Gulik, Stella A. Bult, Pien E.J. de Ruiter, Floortje Huizing, Alexander de Mol van Otterloo, Alexander Leijdesdorff, Sjoerd Lagarde
Surgery.2026; 190: 109843. CrossRef
Training the eye and diagnosing the canvas in the Museum ‘A perspective on art-based medical education’
T.M. van Gulik, S.A. Bult, P.E.J. de Ruiter, F. Huizing, A. Leijdesdorff, S. Lagarde, A. de Mol van Otterloo
Ethics, Medicine and Public Health.2026; 34: 101243. CrossRef
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Hirohisa Fujikawa, Takayuki Ando, Junji Haruta
Medical Science Educator.2025; 35(6): 3105. CrossRef
Erb’s Palsy: Visual Diagnosis in Art before Medical History?
Pien E.J. de Ruiter, Stella A. Bult, Jeroen R. Dijkstra, Thomas M. van Gulik
Gynecologic and Obstetric Investigation.2025; 91(1): 26. CrossRef

GPT-4o’s competency in answering the simulated written European Board of Interventional Radiology exam compared to a medical student and experts in Germany and its ability to generate exam items on interventional radiology: a descriptive study: Sebastian Ebel, Constantin Ehrengut, Timm Denecke, Holger Gößmann, Anne Bettina Beeskow; J Educ Eval Health Prof. 2024;21:21. Published online August 20, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.21

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Abstract PDF Supplementary Material

Purpose
This study aimed to determine whether ChatGPT-4o, a generative artificial intelligence (AI) platform, was able to pass a simulated written European Board of Interventional Radiology (EBIR) exam and whether GPT-4o can be used to train medical students and interventional radiologists of different levels of expertise by generating exam items on interventional radiology.
Methods
GPT-4o was asked to answer 370 simulated exam items of the Cardiovascular and Interventional Radiology Society of Europe (CIRSE) for EBIR preparation (CIRSE Prep). Subsequently, GPT-4o was requested to generate exam items on interventional radiology topics at levels of difficulty suitable for medical students and the EBIR exam. Those generated items were answered by 4 participants, including a medical student, a resident, a consultant, and an EBIR holder. The correctly answered items were counted. One investigator checked the answers and items generated by GPT-4o for correctness and relevance. This work was done from April to July 2024.
Results
GPT-4o correctly answered 248 of the 370 CIRSE Prep items (67.0%). For 50 CIRSE Prep items, the medical student answered 46.0%, the resident 42.0%, the consultant 50.0%, and the EBIR holder 74.0% correctly. All participants answered 82.0% to 92.0% of the 50 GPT-4o generated items at the student level correctly. For the 50 GPT-4o items at the EBIR level, the medical student answered 32.0%, the resident 44.0%, the consultant 48.0%, and the EBIR holder 66.0% correctly. All participants could pass the GPT-4o-generated items for the student level; while the EBIR holder could pass the GPT-4o-generated items for the EBIR level. Two items (0.3%) out of 150 generated by the GPT-4o were assessed as implausible.
Conclusion
GPT-4o could pass the simulated written EBIR exam and create exam items of varying difficulty to train medical students and interventional radiologists.

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When AI meets medical assessment: A comparative study of GPT-4O and Claude 3 Opus in China's standardized resident physician examinations
Wenjie Zhong, Yidan Hu, Ruiqiang Su, Lingcong Xu, Yating Chen, Niezhenghao He, Caiyuan Liu, Ke Xu, Mao Zhao, Wenao Liao, Wei Zhang, Jiang Hu, Fei Wang, Haowen Cui
Computers in Human Behavior Reports.2026; 21: 100974. CrossRef
The current status and future prospects of artificial intelligence education in residency training
Hongsen Zhang, Kun Qian, Jing Wang, Chuansheng Zheng
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Comparative analysis of multimodal large language models GPT-4o and o1 versus clinicians in clinical case challenge questions: Retrospective cross-sectional study
Jaewon Jung, Hyunjae Kim, SungA Bae, Jin Young Park
Medicine.2026; 105(4): e47071. CrossRef
Validity of AI-generated multiple-choice questions in medical education: a systematic review
Yavuz Selim Kıyak, Abdullah Bedir Kaya, Emre Emekli
Postgraduate Medical Journal.2026;[Epub] CrossRef
Evaluating the performance of ChatGPT in patient consultation and image-based preliminary diagnosis in thyroid eye disease
Yue Wang, Shuo Yang, Chengcheng Zeng, Yingwei Xie, Ya Shen, Jian Li, Xiao Huang, Ruili Wei, Yuqing Chen
Frontiers in Medicine.2025;[Epub] CrossRef
Solving Complex Pediatric Surgical Case Studies: A Comparative Analysis of Copilot, ChatGPT-4, and Experienced Pediatric Surgeons' Performance
Richard Gnatzy, Martin Lacher, Michael Berger, Michael Boettcher, Oliver J. Deffaa, Joachim Kübler, Omid Madadi-Sanjani, Illya Martynov, Steffi Mayer, Mikko P. Pakarinen, Richard Wagner, Tomas Wester, Augusto Zani, Ophelia Aubert
European Journal of Pediatric Surgery.2025; 35(05): 382. CrossRef
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Shiwei Li, Jun Jiang, Xiaodong Yang
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Andrea Lastrucci, Nicola Iosca, Yannick Wandael, Angelo Barra, Graziano Lepri, Nevio Forini, Renzo Ricci, Vittorio Miele, Daniele Giansanti
Diagnostics.2025; 15(7): 893. CrossRef
Evaluating the performance of GPT-3.5, GPT-4, and GPT-4o in the Chinese National Medical Licensing Examination
Dingyuan Luo, Mengke Liu, Runyuan Yu, Yulian Liu, Wenjun Jiang, Qi Fan, Naifeng Kuang, Qiang Gao, Tao Yin, Zuncheng Zheng
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Evaluating Large Language Models for Preoperative Patient Education in Superior Capsular Reconstruction: Comparative Study of Claude, GPT, and Gemini
Yukang Liu, Hua Li, Jianfeng Ouyang, Zhaowen Xue, Min Wang, Hebei He, Bin Song, Xiaofei Zheng, Wenyi Gan
JMIR Perioperative Medicine.2025; 8: e70047. CrossRef
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Dan Nguyen, Grace Hyun J. Kim, Arash Bedayat
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Performance of ChatGPT-4 on the French Board of Plastic Reconstructive and Aesthetic Surgery written exam: a descriptive study
Emma Dejean-Bouyer, Anoujat Kanlagna, François Thuau, Pierre Perrot, Ugo Lancien
Journal of Educational Evaluation for Health Professions.2025; 22: 27. CrossRef
Technologies, opportunities, challenges, and future directions for integrating generative artificial intelligence into medical education: a narrative review
Junseok Kang, Jihyun Ahn
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BMC Medical Education.2024;[Epub] CrossRef

Impact of a change from A–F grading to honors/pass/fail grading on academic performance at Yonsei University College of Medicine in Korea: a cross-sectional serial mediation analysis: Min-Kyeong Kim, Hae Won Kim; J Educ Eval Health Prof. 2024;21:20. Published online August 16, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.20; Correction in: J Educ Eval Health Prof 2024;21(0):35

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Purpose
This study aimed to explore how the grading system affected medical students’ academic performance based on their perceptions of the learning environment and intrinsic motivation in the context of changing from norm-referenced A–F grading to criterion-referenced honors/pass/fail grading.
Methods
The study involved 238 second-year medical students from 2014 (n=127, A–F grading) and 2015 (n=111, honors/pass/fail grading) at Yonsei University College of Medicine in Korea. Scores on the Dundee Ready Education Environment Measure, the Academic Motivation Scale, and the Basic Medical Science Examination were used to measure overall learning environment perceptions, intrinsic motivation, and academic performance, respectively. Serial mediation analysis was conducted to examine the pathways between the grading system and academic performance, focusing on the mediating roles of student perceptions and intrinsic motivation.
Results
The honors/pass/fail grading class students reported more positive perceptions of the learning environment, higher intrinsic motivation, and better academic performance than the A–F grading class students. Mediation analysis demonstrated a serial mediation effect between the grading system and academic performance through learning environment perceptions and intrinsic motivation. Student perceptions and intrinsic motivation did not independently mediate the relationship between the grading system and performance.
Conclusion
Reducing the number of grades and eliminating rank-based grading might have created an affirming learning environment that fulfills basic psychological needs and reinforces the intrinsic motivation linked to academic performance. The cumulative effect of these 2 mediators suggests that a comprehensive approach should be used to understand student performance.

Citations

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Alternative grading approaches in health professions education: a scoping review protocol
Elena Wong Espiritu, Aine O’Connor, Sara Blass, Kathryn L. Dambrino, Angela Shelton Clauson
JBI Evidence Synthesis.2025; 23(11): 2301. CrossRef
Erratum: Impact of a change from A–F grading to honors/pass/fail grading on academic performance at Yonsei University College of Medicine in Korea: a cross-sectional serial mediation analysis

Journal of Educational Evaluation for Health Professions.2024; 21: 35. CrossRef

Special article on the 20th anniversary of the journal

Comparison of real data and simulated data analysis of a stopping rule based on the standard error of measurement in computerized adaptive testing for medical examinations in Korea: a psychometric study: Dong Gi Seo, Jeongwook Choi, Jinha Kim; J Educ Eval Health Prof. 2024;21:18. Published online July 9, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.18

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Purpose
This study aimed to compare and evaluate the efficiency and accuracy of computerized adaptive testing (CAT) under 2 stopping rules (standard error of measurement [SEM]=0.3 and 0.25) using both real and simulated data in medical examinations in Korea.
Methods
This study employed post-hoc simulation and real data analysis to explore the optimal stopping rule for CAT in medical examinations. The real data were obtained from the responses of 3rd-year medical students during examinations in 2020 at Hallym University College of Medicine. Simulated data were generated using estimated parameters from a real item bank in R. Outcome variables included the number of examinees’ passing or failing with SEM values of 0.25 and 0.30, the number of items administered, and the correlation. The consistency of real CAT result was evaluated by examining consistency of pass or fail based on a cut score of 0.0. The efficiency of all CAT designs was assessed by comparing the average number of items administered under both stopping rules.
Results
Both SEM 0.25 and SEM 0.30 provided a good balance between accuracy and efficiency in CAT. The real data showed minimal differences in pass/fail outcomes between the 2 SEM conditions, with a high correlation (r=0.99) between ability estimates. The simulation results confirmed these findings, indicating similar average item numbers between real and simulated data.
Conclusion
The findings suggest that both SEM 0.25 and 0.30 are effective termination criteria in the context of the Rasch model, balancing accuracy and efficiency in CAT.

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Review

Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review: Xiaojun Xu, Yixiao Chen, Jing Miao; J Educ Eval Health Prof. 2024;21:6. Published online March 15, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.6

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Abstract PDF Supplementary Material

Background
ChatGPT is a large language model (LLM) based on artificial intelligence (AI) capable of responding in multiple languages and generating nuanced and highly complex responses. While ChatGPT holds promising applications in medical education, its limitations and potential risks cannot be ignored.
Methods
A scoping review was conducted for English articles discussing ChatGPT in the context of medical education published after 2022. A literature search was performed using PubMed/MEDLINE, Embase, and Web of Science databases, and information was extracted from the relevant studies that were ultimately included.
Results
ChatGPT exhibits various potential applications in medical education, such as providing personalized learning plans and materials, creating clinical practice simulation scenarios, and assisting in writing articles. However, challenges associated with academic integrity, data accuracy, and potential harm to learning were also highlighted in the literature. The paper emphasizes certain recommendations for using ChatGPT, including the establishment of guidelines. Based on the review, 3 key research areas were proposed: cultivating the ability of medical students to use ChatGPT correctly, integrating ChatGPT into teaching activities and processes, and proposing standards for the use of AI by medical students.
Conclusion
ChatGPT has the potential to transform medical education, but careful consideration is required for its full integration. To harness the full potential of ChatGPT in medical education, attention should not only be given to the capabilities of AI but also to its impact on students and teachers.

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Health Systems.2026; 15(2): 125. CrossRef
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Yuhong Shi, Kun Yu, Yifei Dong, Fang Chen
Computers and Education: Artificial Intelligence.2026; 10: 100529. CrossRef
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Amrita Ganguly, Nafisa Mehjabin, Aqdas Malik, Aditya Johri
AI and Ethics.2026;[Epub] CrossRef
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Che-Wei Hsu, Mia Cochrane, Sasini Bambarawana
International Journal of Mental Health.2026; : 1. CrossRef
Medical Student Experiences With ChatGPT: National Cross-Sectional Study
Alan Yuesheng Xu, Skye Speakman, Vincent Salvatore Piranio, Robert Medina, Michelle Liu, Chris Lamprecht, Nicolas Abchee, Meghan Brennan
JMIR Formative Research.2026; 10: e76838. CrossRef
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Luoyu Lian, Xin Luo, Kavimbi Chipusu, Muhammad Awais Ashraf, Kelvin K. L. Wong, Wenjun Zhang
Bioengineering.2026; 13(1): 113. CrossRef
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Zhitao Hou, Jing Chen, Hongwei Guo
Frontiers in Medicine.2026;[Epub] CrossRef
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John Obeid, Christopher Bobier, Alex Gillham, Adam Omelianchuk, Daniel Hurst
BMC Medical Education.2026;[Epub] CrossRef
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Deniz Oke, Ozge Gulsum Illeez, Esra Giray, Betül Çiftçi
Diagnostics.2026; 16(4): 589. CrossRef
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Song-Bin Guo, Deng-Yao Liu, Xiao-Jie Fang, Yuan Meng, Zhen-Zhong Zhou, Jing Li, Mei Li, Li-Ling Luo, Hai-Long Li, Xiu-Yu Cai, Wei-Juan Huang, Xiao-Peng Tian
International Journal of Surgery.2026; 112(2): 2805. CrossRef
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Christian J. Wiedermann, Giuliano Piccoliori, Pietro Murali, Cristina Pizzini, Doris Hager von Strobele Prainsack
Healthcare.2026; 14(4): 503. CrossRef
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Aytug Onan, Arbi Haza Nasution, Tugba Celikten, Pinar Cetin
IEEE Access.2026; 14: 32807. CrossRef
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Uludağ Üniversitesi Tıp Fakültesi Dergisi.2026; 52: 1870116. CrossRef
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Anil Ozgun Karatekin
European Journal of Dental Education.2026;[Epub] CrossRef
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JMIR Medical Education.2026; 12: e85373. CrossRef
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Journal of Educational Evaluation for Health Professions.2026; 23: 4. CrossRef
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BMC Medical Education.2026;[Epub] CrossRef
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Selina Hasan, Ayesha Ahmed, Faisal Ismail
Advances in Medical Education and Practice.2026; Volume 17: 1. CrossRef
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The Journal of Prosthetic Dentistry.2026;[Epub] CrossRef
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BMC Medical Education.2026;[Epub] CrossRef
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Haroon Saloojee, Michael C. Gramanie, Rhodasi Mwale, Bruce A. Bassett, Shabir A. Madhi, Ismail S. Kalla
Medical Teacher.2026; : 1. CrossRef
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Frontiers in Public Health.2026;[Epub] CrossRef
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Expert Systems with Applications.2026; 329: 132966. CrossRef
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Medicine.2026; 105(22): e49128. CrossRef
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BMC Medical Education.2025;[Epub] CrossRef
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Software Impacts.2025; 23: 100742. CrossRef
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Advances in Physiology Education.2025; 49(2): 398. CrossRef
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Frontiers in Education.2025;[Epub] CrossRef
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Cureus.2025;[Epub] CrossRef
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Open Praxis.2025; 17(1): 129. CrossRef
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BMC Medical Education.2025;[Epub] CrossRef
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Frontiers of Digital Education.2025;[Epub] CrossRef
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Scientific Reports.2025;[Epub] CrossRef
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Computers & Education.2025; 237: 105388. CrossRef
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The American Surgeon™.2025; 91(10): 1729. CrossRef
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Research articles

Discovering social learning ecosystems during clinical clerkship from United States medical students’ feedback encounters: a content analysis: Anna Therese Cianciolo, Heeyoung Han, Lydia Anne Howes, Debra Lee Klamen, Sophia Matos; J Educ Eval Health Prof. 2024;21:5. Published online February 28, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.5

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Abstract PDF Supplementary Material: Purpose
We examined United States medical students’ self-reported feedback encounters during clerkship training to better understand in situ feedback practices. Specifically, we asked: Who do students receive feedback from, about what, when, where, and how do they use it? We explored whether curricular expectations for preceptors’ written commentary aligned with feedback as it occurs naturalistically in the workplace.
Methods
This study occurred from July 2021 to February 2022 at Southern Illinois University School of Medicine. We used qualitative survey-based experience sampling to gather students’ accounts of their feedback encounters in 8 core specialties. We analyzed the who, what, when, where, and why of 267 feedback encounters reported by 11 clerkship students over 30 weeks. Code frequencies were mapped qualitatively to explore patterns in feedback encounters.
Results
Clerkship feedback occurs in patterns apparently related to the nature of clinical work in each specialty. These patterns may be attributable to each specialty’s “social learning ecosystem”—the distinctive learning environment shaped by the social and material aspects of a given specialty’s work, which determine who preceptors are, what students do with preceptors, and what skills or attributes matter enough to preceptors to comment on.
Conclusion
Comprehensive, standardized expectations for written feedback across specialties conflict with the reality of workplace-based learning. Preceptors may be better able—and more motivated—to document student performance that occurs as a natural part of everyday work. Nurturing social learning ecosystems could facilitate workplace-based learning such that, across specialties, students acquire a comprehensive clinical skillset appropriate for graduation.

Medical students’ patterns of using ChatGPT as a feedback tool and perceptions of ChatGPT in a Leadership and Communication course in Korea: a cross-sectional study: Janghee Park; J Educ Eval Health Prof. 2023;20:29. Published online November 10, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.29

9,073 View
319 Download
17 Web of Science
19 Crossref

Abstract PDF Supplementary Material

Purpose
This study aimed to analyze patterns of using ChatGPT before and after group activities and to explore medical students’ perceptions of ChatGPT as a feedback tool in the classroom.
Methods
The study included 99 2nd-year pre-medical students who participated in a “Leadership and Communication” course from March to June 2023. Students engaged in both individual and group activities related to negotiation strategies. ChatGPT was used to provide feedback on their solutions. A survey was administered to assess students’ perceptions of ChatGPT’s feedback, its use in the classroom, and the strengths and challenges of ChatGPT from May 17 to 19, 2023.
Results
The students responded by indicating that ChatGPT’s feedback was helpful, and revised and resubmitted their group answers in various ways after receiving feedback. The majority of respondents expressed agreement with the use of ChatGPT during class. The most common response concerning the appropriate context of using ChatGPT’s feedback was “after the first round of discussion, for revisions.” There was a significant difference in satisfaction with ChatGPT’s feedback, including correctness, usefulness, and ethics, depending on whether or not ChatGPT was used during class, but there was no significant difference according to gender or whether students had previous experience with ChatGPT. The strongest advantages were “providing answers to questions” and “summarizing information,” and the worst disadvantage was “producing information without supporting evidence.”
Conclusion
The students were aware of the advantages and disadvantages of ChatGPT, and they had a positive attitude toward using ChatGPT in the classroom.

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Applications and Outcomes of Large‑Language‑Model‑Generated Feedback in Undergraduate Medical Education: A Scoping Review
Yavuz Selim Kıyak, Tuğba İş-Kara, Emre Emekli
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Attitudes and perceptions of the application of large language models among health professionals: A mixed-methods systematic review
Wen Luo, Tao Feng, Ting Zhang, Xinyu Chen, Xianying Lu, Yuhang Li, Chaoming Hou, Jing Gao
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Generative AI's Impact on the Mental Health of Medical Students: Scenario Analysis
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Higher education students’ perceptions of ChatGPT: A global study of early reactions
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Development and validation of the student ratings in clinical teaching scale in Australia: a methodological study: Pin-Hsiang Huang, Anthony John O’Sullivan, Boaz Shulruf; J Educ Eval Health Prof. 2023;20:26. Published online September 5, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.26

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197 Download
1 Web of Science

Abstract PDF Supplementary Material: Purpose
This study aimed to devise a valid measurement for assessing clinical students’ perceptions of teaching practices.
Methods
A new tool was developed based on a meta-analysis encompassing effective clinical teaching-learning factors. Seventy-nine items were generated using a frequency (never to always) scale. The tool was applied to the University of New South Wales year 2, 3, and 6 medical students. Exploratory and confirmatory factor analysis (exploratory factor analysis [EFA] and confirmatory factor analysis [CFA], respectively) were conducted to establish the tool’s construct validity and goodness of fit, and Cronbach’s α was used for reliability.
Results
In total, 352 students (44.2%) completed the questionnaire. The EFA identified student-centered learning, problem-solving learning, self-directed learning, and visual technology (reliability, 0.77 to 0.89). CFA showed acceptable goodness of fit (chi-square P<0.01, comparative fit index=0.930 and Tucker-Lewis index=0.917, root mean square error of approximation=0.069, standardized root mean square residual=0.06).
Conclusion
The established tool—Student Ratings in Clinical Teaching (STRICT)—is a valid and reliable tool that demonstrates how students perceive clinical teaching efficacy. STRICT measures the frequency of teaching practices to mitigate the biases of acquiescence and social desirability. Clinical teachers may use the tool to adapt their teaching practices with more active learning activities and to utilize visual technology to facilitate clinical learning efficacy. Clinical educators may apply STRICT to assess how these teaching practices are implemented in current clinical settings.

Experience of introducing an electronic health records station in an objective structured clinical examination to evaluate medical students’ communication skills in Canada: a descriptive study: Kuan-chin Jean Chen, Ilona Bartman, Debra Pugh, David Topps, Isabelle Desjardins, Melissa Forgie, Douglas Archibald; J Educ Eval Health Prof. 2023;20:22. Published online July 4, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.22

7,076 View
187 Download
2 Web of Science
2 Crossref

Abstract PDF Supplementary Material

Purpose
There is limited literature related to the assessment of electronic medical record (EMR)-related competencies. To address this gap, this study explored the feasibility of an EMR objective structured clinical examination (OSCE) station to evaluate medical students’ communication skills by psychometric analyses and standardized patients’ (SPs) perspectives on EMR use in an OSCE.
Methods
An OSCE station that incorporated the use of an EMR was developed and pilot-tested in March 2020. Students’ communication skills were assessed by SPs and physician examiners. Students’ scores were compared between the EMR station and 9 other stations. A psychometric analysis, including item total correlation, was done. SPs participated in a post-OSCE focus group to discuss their perception of EMRs’ effect on communication.
Results
Ninety-nine 3rd-year medical students participated in a 10-station OSCE that included the use of the EMR station. The EMR station had an acceptable item total correlation (0.217). Students who leveraged graphical displays in counseling received higher OSCE station scores from the SPs (P=0.041). The thematic analysis of SPs’ perceptions of students’ EMR use from the focus group revealed the following domains of themes: technology, communication, case design, ownership of health information, and timing of EMR usage.
Conclusion
This study demonstrated the feasibility of incorporating EMR in assessing learner communication skills in an OSCE. The EMR station had acceptable psychometric characteristics. Some medical students were able to efficiently use the EMRs as an aid in patient counseling. Teaching students how to be patient-centered even in the presence of technology may promote engagement.

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Fahad Abdulaziz Alrashed, Tauseef Ahmad, Abdulrahman M. Alsubiheen, Saad A. Alhammad, Mishal M. Aldaihan, Alaa M. Albishi, Zafrul Hasan
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Usage and perception of electronic medical records (EMR) among medical students in southwestern Nigeria
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What impacts students’ satisfaction the most from Medicine Student Experience Questionnaire in Australia: a validity study: Pin-Hsiang Huang, Gary Velan, Greg Smith, Melanie Fentoullis, Sean Edward Kennedy, Karen Jane Gibson, Kerry Uebel, Boaz Shulruf; J Educ Eval Health Prof. 2023;20:2. Published online January 18, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.2

5,386 View
205 Download
4 Web of Science
3 Crossref

Abstract PDF Supplementary Material

Purpose
This study evaluated the validity of student feedback derived from Medicine Student Experience Questionnaire (MedSEQ), as well as the predictors of students’ satisfaction in the Medicine program.
Methods
Data from MedSEQ applying to the University of New South Wales Medicine program in 2017, 2019, and 2021 were analyzed. Confirmatory factor analysis (CFA) and Cronbach’s α were used to assess the construct validity and reliability of MedSEQ respectively. Hierarchical multiple linear regressions were used to identify the factors that most impact students’ overall satisfaction with the program.
Results
A total of 1,719 students (34.50%) responded to MedSEQ. CFA showed good fit indices (root mean square error of approximation=0.051; comparative fit index=0.939; chi-square/degrees of freedom=6.429). All factors yielded good (α>0.7) or very good (α>0.8) levels of reliability, except the “online resources” factor, which had acceptable reliability (α=0.687). A multiple linear regression model with only demographic characteristics explained 3.8% of the variance in students’ overall satisfaction, whereas the model adding 8 domains from MedSEQ explained 40%, indicating that 36.2% of the variance was attributable to students’ experience across the 8 domains. Three domains had the strongest impact on overall satisfaction: “being cared for,” “satisfaction with teaching,” and “satisfaction with assessment” (β=0.327, 0.148, 0.148, respectively; all with P<0.001).
Conclusion
MedSEQ has good construct validity and high reliability, reflecting students’ satisfaction with the Medicine program. Key factors impacting students’ satisfaction are the perception of being cared for, quality teaching irrespective of the mode of delivery and fair assessment tasks which enhance learning.

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Brief report

Are ChatGPT’s knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study: Sun Huh; J Educ Eval Health Prof. 2023;20:1. Published online January 11, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.1

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1,251 Download
261 Web of Science
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Abstract PDF Supplementary Material

This study aimed to compare the knowledge and interpretation ability of ChatGPT, a language model of artificial general intelligence, with those of medical students in Korea by administering a parasitology examination to both ChatGPT and medical students. The examination consisted of 79 items and was administered to ChatGPT on January 1, 2023. The examination results were analyzed in terms of ChatGPT’s overall performance score, its correct answer rate by the items’ knowledge level, and the acceptability of its explanations of the items. ChatGPT’s performance was lower than that of the medical students, and ChatGPT’s correct answer rate was not related to the items’ knowledge level. However, there was a relationship between acceptable explanations and correct answers. In conclusion, ChatGPT’s knowledge and interpretation ability for this parasitology examination were not yet comparable to those of medical students in Korea.

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Aesthetic Surgery Journal.2024; 44(3): 329. CrossRef
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Ana Suárez, Víctor Díaz‐Flores García, Juan Algar, Margarita Gómez Sánchez, María Llorente de Pedro, Yolanda Freire
International Endodontic Journal.2024; 57(1): 108. CrossRef
Bob or Bot: Exploring ChatGPT's Answers to University Computer Science Assessment
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ACM Transactions on Computing Education.2024; 24(1): 1. CrossRef
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Biochemistry and Molecular Biology Education.2024; 52(2): 237. CrossRef
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Michelle W. T. Cheng, Iris H. Y. YIM
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Firas Haddad, Joanna S Saade
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Jai Ranjan, Absar Ahmad, Monalisa Subudhi, Ajay Kumar
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JMIR Medical Education.2024; 10: e50965. CrossRef
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Lung‐Hsiang Wong, Hyejin Park, Chee‐Kit Looi
Journal of Computer Assisted Learning.2024; 40(4): 1428. CrossRef
A scoping review of artificial intelligence in medical education: BEME Guide No. 84
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Medical Teacher.2024; 46(4): 446. CrossRef
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Omar Mubin, Fady Alnajjar, Zouheir Trabelsi, Luqman Ali, Medha Mohan Ambali Parambil, Zhao Zou
IEEE Access.2024; 12: 30518. CrossRef
Potential applications of ChatGPT in obstetrics and gynecology in Korea: a review article
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Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review
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Journal of Health Sciences and Medicine.2024; 7(2): 224. CrossRef
Performance of ChatGPT on the Korean National Examination for Dental Hygienists
Soo-Myoung Bae, Hye-Rim Jeon, Gyoung-Nam Kim, Seon-Hui Kwak, Hyo-Jin Lee
Journal of Dental Hygiene Science.2024; 24(1): 62. CrossRef
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Sultan Ayoub Meo, Metib Alotaibi, Muhammad Zain Sultan Meo, Muhammad Omair Sultan Meo, Mashhood Hamid
Frontiers in Public Health.2024;[Epub] CrossRef
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Adnan Kasapovic, Thaer Ali, Mari Babasiz, Jessica Bojko, Martin Gathen, Robert Kaczmarczyk, Jonas Roos
Cureus.2024;[Epub] CrossRef
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Daniel Gooch, Kevin Waugh, Mike Richards, Mark Slaymaker, John Woodthorpe
ACM Inroads.2024; 15(2): 39. CrossRef
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Advances in Medical Education and Practice.2024; Volume 15: 393. CrossRef
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Sun Huh
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Mahdi Zarei, Maryam Zarei, Sina Hamzehzadeh, Sepehr Shakeri Bavil Oliyaei, Mohammad-Salar Hosseini
Shiraz E-Medical Journal.2024;[Epub] CrossRef
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Farah Naja, Mandy Taktouk, Dana Matbouli, Sharfa Khaleel, Ayah Maher, Berna Uzun, Maryam Alameddine, Lara Nasreddine
European Journal of Clinical Nutrition.2024; 78(10): 887. CrossRef
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Andrea Moglia, Konstantinos Georgiou, Pietro Cerveri, Luca Mainardi, Richard M. Satava, Alfred Cuschieri
Artificial Intelligence Review.2024;[Epub] CrossRef
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Annika Meyer, Ari Soleman, Janik Riese, Thomas Streichert
Clinical Chemistry and Laboratory Medicine (CCLM).2024;[Epub] CrossRef
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Hye Kyung Jin, Ha Eun Lee, EunYoung Kim
BMC Medical Education.2024;[Epub] CrossRef
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Pratik Surana, Priyanka P. Ostwal, Shruti Vishal Dev, Jayesh Tiwari, Kadire Shiva Charan Yadav, Gajji Renuka
Research Journal of Pharmacy and Technology.2024; : 3489. CrossRef
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Solmoe Ahn, Jeongyoon Lee, Jungmin Park, Soyoung Jung, Jihoon Song
The Journal of Korean Association of Computer Education.2024; 27(6): 11. CrossRef
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JMIR Medical Education.2024; 10: e57451. CrossRef
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Eun-Young Jeon, Hyun-Na Ahn, Jeong-Hyun Lee
Journal of Dental Hygiene Science.2024; 24(4): 309. CrossRef
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Berin Doru, Christoph Maier, Johanna Sophie Busse, Thomas Lücke, Judith Schönhoff, Elena Enax- Krumova, Steffen Hessler, Maria Berger, Marianne Tokic
JMIR Medical Education.2024;[Epub] CrossRef
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Amal Abdullah Alibrahim
South African Journal of Education.2024; 44(4): 1. CrossRef
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Muskan Dubey, Arun Kumar Dubey , Ravindra P Veeranna
Trends in Scholarly Publishing.2024; 3(1): 1. CrossRef
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Abubaker Qutieshat, Alreem Al Rusheidi, Samiya Al Ghammari, Abdulghani Alarabi, Abdurahman Salem, Maja Zelihic
Diagnosis.2024; 11(3): 259. CrossRef
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Ranwir K Sinha, Asitava Deb Roy, Nikhil Kumar, Himel Mondal
Cureus.2023;[Epub] CrossRef
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Sun Huh
Journal of Educational Evaluation for Health Professions.2023; 20: 5. CrossRef
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Sun Huh
Science Editing.2023; 10(1): 1. CrossRef
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Dipmala Das, Nikhil Kumar, Langamba Angom Longjam, Ranwir Sinha, Asitava Deb Roy, Himel Mondal, Pratima Gupta
Cureus.2023;[Epub] CrossRef
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Arindam Ghosh, Aritri Bir
Cureus.2023;[Epub] CrossRef
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Omar Temsah, Samina A Khan, Yazan Chaiah, Abdulrahman Senjab, Khalid Alhasan, Amr Jamal, Fadi Aljamaan, Khalid H Malki, Rabih Halwani, Jaffar A Al-Tawfiq, Mohamad-Hani Temsah, Ayman Al-Eyadhy
Cureus.2023;[Epub] CrossRef
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Bader Fatani
Cureus.2023;[Epub] CrossRef
ChatGPT in Dentistry: A Comprehensive Review
Hind M Alhaidry, Bader Fatani, Jenan O Alrayes, Aljowhara M Almana, Nawaf K Alfhaed
Cureus.2023;[Epub] CrossRef
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Sun Huh
Journal of the Korean Medical Association.2023; 66(4): 218. CrossRef
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JMIR Medical Education.2023; 9: e48291. CrossRef
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Sam Sedaghat
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Advances in Education.2023; 13(05): 2617. CrossRef
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JMIR Medical Education.2023; 9: e48002. CrossRef
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European Archives of Oto-Rhino-Laryngology.2023; 280(9): 4271. CrossRef
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Mayank Agarwal, Priyanka Sharma, Ayan Goswami
Cureus.2023;[Epub] CrossRef
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Rebecca Shin-Yee Wong, Long Chiau Ming, Raja Affendi Raja Ali
JMIR Medical Education.2023; 9: e47274. CrossRef
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Journal of Educational Evaluation for Health Professions.2023; 20: 17. CrossRef
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JMIR Medical Education.2023; 9: e48433. CrossRef
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Cureus.2023;[Epub] CrossRef
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Cureus.2023;[Epub] CrossRef
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JMIR Medical Education.2023; 9: e48785. CrossRef
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Cureus.2023;[Epub] CrossRef
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JMIR Medical Education.2023; 9: e50658. CrossRef
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Reviews

Factors associated with medical students’ scores on the National Licensing Exam in Peru: a systematic review: Javier Alejandro Flores-Cohaila; J Educ Eval Health Prof. 2022;19:38. Published online December 29, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.38

8,919 View
396 Download
3 Web of Science
6 Crossref

Abstract PDF Supplementary Material

Purpose
This study aimed to identify factors that have been studied for their associations with National Licensing Examination (ENAM) scores in Peru.
Methods
A search was conducted of literature databases and registers, including EMBASE, SciELO, Web of Science, MEDLINE, Peru’s National Register of Research Work, and Google Scholar. The following key terms were used: “ENAM” and “associated factors.” Studies in English and Spanish were included. The quality of the included studies was evaluated using the Medical Education Research Study Quality Instrument (MERSQI).
Results
In total, 38,500 participants were enrolled in 12 studies. Most (11/12) studies were cross-sectional, except for one case-control study. Three studies were published in peer-reviewed journals. The mean MERSQI was 10.33. A better performance on the ENAM was associated with a higher-grade point average (GPA) (n=8), internship setting in EsSalud (n=4), and regular academic status (n=3). Other factors showed associations in various studies, such as medical school, internship setting, age, gender, socioeconomic status, simulations test, study resources, preparation time, learning styles, study techniques, test-anxiety, and self-regulated learning strategies.
Conclusion
The ENAM is a multifactorial phenomenon; our model gives students a locus of control on what they can do to improve their score (i.e., implement self-regulated learning strategies) and faculty, health policymakers, and managers a framework to improve the ENAM score (i.e., design remediation programs to improve GPA and integrate anxiety-management courses into the curriculum).

Citations

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Anxiety and Depression Associated With the Dependent Use of Generative AI in Medical Students: Cross-Sectional Study
Janett V Chavez Sosa, Salomon Huancahuire-Vega
JMIR Formative Research.2026; 10: e82667. CrossRef
Peruvian medical residency selection: a portrayal of scores, distribution, and predictors of 28,872 applicants between 2019 and 2023
Javier A. Flores-Cohaila, Brayan Miranda-Chavez, Cesar Copaja-Corzo, Xiomara C. Benavente-Chalco, Wagner Rios-García, Vanessa P. Moreno-Ccama, Angel Samanez-Obeso, Marco Rivarola-Hidalgo
BMC Medical Education.2025;[Epub] CrossRef
Puntajes en pruebas de progreso como predictores del desempeño en el Examen Nacional de Medicina del Perú
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Javier A Flores-Cohaila, Abigaíl García-Vicente, Sonia F Vizcarra-Jiménez, Janith P De la Cruz-Galán, Jesús D Gutiérrez-Arratia, Blanca Geraldine Quiroga Torres, Alvaro Taype-Rondan
JMIR Medical Education.2023; 9: e48039. CrossRef

Medical students’ satisfaction level with e-learning during the COVID-19 pandemic and its related factors: a systematic review: Mahbubeh Tabatabaeichehr, Samane Babaei, Mahdieh Dartomi, Peiman Alesheikh, Amir Tabatabaee, Hamed Mortazavi, Zohreh Khoshgoftar; J Educ Eval Health Prof. 2022;19:37. Published online December 20, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.37

6,891 View
295 Download
24 Web of Science
26 Crossref

Abstract PDF Supplementary Material

Purpose
This review investigated medical students’ satisfaction level with e-learning during the coronavirus disease 2019 (COVID-19) pandemic and its related factors.
Methods
A comprehensive systematic search was performed of international literature databases, including Scopus, PubMed, Web of Science, and Persian databases such as Iranmedex and Scientific Information Database using keywords extracted from Medical Subject Headings such as “Distance learning,” “Distance education,” “Online learning,” “Online education,” and “COVID-19” from the earliest date to July 10, 2022. The quality of the studies included in this review was evaluated using the appraisal tool for cross-sectional studies (AXIS tool).
Results
A total of 15,473 medical science students were enrolled in 24 studies. The level of satisfaction with e-learning during the COVID-19 pandemic among medical science students was 51.8%. Factors such as age, gender, clinical year, experience with e-learning before COVID-19, level of study, adaptation content of course materials, interactivity, understanding of the content, active participation of the instructor in the discussion, multimedia use in teaching sessions, adequate time dedicated to the e-learning, stress perception, and convenience had significant relationships with the satisfaction of medical students with e-learning during the COVID-19 pandemic.
Conclusion
Therefore, due to the inevitability of online education and e-learning, it is suggested that educational managers and policymakers choose the best online education method for medical students by examining various studies in this field to increase their satisfaction with e-learning.

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Brief report

Self-directed learning quotient and common learning types of pre-medical students in Korea by the Multi-Dimensional Learning Strategy Test 2nd edition: a descriptive study: Sun Kim, A Ra Cho, Chul Woon Chung; J Educ Eval Health Prof. 2022;19:32. Published online November 28, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.32

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Abstract PDF Supplementary Material: This study aimed to find the self-directed learning quotient and common learning types of pre-medical students through the confirmation of 4 characteristics of learning strategies, including personality, motivation, emotion, and behavior. The response data were collected from 277 out of 294 target first-year pre-medical students from 2019 to 2021, using the Multi-Dimensional Learning Strategy Test 2nd edition. The most common learning type was a self-directed type (44.0%), stagnant type (33.9%), latent type (14.4%), and conscientiousness type (7.6%). The self-directed learning index was high (29.2%), moderate (24.6%), somewhat high (21.7%), somewhat low (14.4%), and low (10.1%). This study confirmed that many students lacked self-directed learning capabilities for learning strategies. In addition, it was found that the difficulties experienced by each student were different, and the variables resulting in difficulties were also diverse. It may provide insights into how to develop programs that can help students increase their self-directed learning capability.

Research articles

Is online objective structured clinical examination teaching an acceptable replacement in post-COVID-19 medical education in the United Kingdom?: a descriptive study: Vashist Motkur, Aniket Bharadwaj, Nimalesh Yogarajah; J Educ Eval Health Prof. 2022;19:30. Published online November 7, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.30

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Abstract PDF Supplementary Material

Purpose
Coronavirus disease 2019 (COVID-19) restrictions resulted in an increased emphasis on virtual communication in medical education. This study assessed the acceptability of virtual teaching in an online objective structured clinical examination (OSCE) series and its role in future education.
Methods
Six surgical OSCE stations were designed, covering common surgical topics, with specific tasks testing data interpretation, clinical knowledge, and communication skills. These were delivered via Zoom to students who participated in student/patient/examiner role-play. Feedback was collected by asking students to compare online teaching with previous experiences of in-person teaching. Descriptive statistics were used for Likert response data, and thematic analysis for free-text items.
Results
Sixty-two students provided feedback, with 81% of respondents finding online instructions preferable to paper equivalents. Furthermore, 65% and 68% found online teaching more efficient and accessible, respectively, than in-person teaching. Only 34% found communication with each other easier online; Forty percent preferred online OSCE teaching to in-person teaching. Students also expressed feedback in positive and negative free-text comments.
Conclusion
The data suggested that generally students were unwilling for online teaching to completely replace in-person teaching. The success of online teaching was dependent on the clinical skill being addressed; some were less amenable to a virtual setting. However, online OSCE teaching could play a role alongside in-person teaching.

Citations

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Perception of online learning, knowledge assessment, and clinical skills among third-year ophthalmology residents studying during the COVID-19 pandemic in Thailand
Wanicha Chuenkongkaew, Jimethat Chalermpong, Weerawat Kiddee, Prut Hanutsaha, Anita Manassakorn, Olan Suwan-apichon, Sakchai Vongkittirux, Raveewan Choontanom, Pittaya Phamonvaechavan, Tharnsook Kasemsup, Paradee Kunavisarut, Sudawadee Somboonthanakij, Su
Scientific Reports.2026;[Epub] CrossRef
The assessment of clinical competence in medical students during the COVID-19 pandemic: a scoping review
Harry McGrath, Elysha Brennan, Dominic Harmon
Irish Journal of Medical Science (1971 -).2026;[Epub] CrossRef
Evaluation of a virtual objective structured clinical examination in the metaverse (Second Life) to assess the clinical skills in emergency radiology of medical students in Spain: a cross-sectional study
Alba Virtudes Perez-Baena, Teodoro Rudolphi-Solero, Rocio Lorenzo-Alvarez, Dolores Dominguez-Pinos, Miguel Jose Ruiz-Gomez, Francisco Sendra-Portero
Journal of Educational Evaluation for Health Professions.2025; 22: 12. CrossRef
A Comparative Study of Student Perspectives on Online Versus In-Person Objective Structured Clinical Examination (OSCE) Teaching at a Medical School in London
Nimalesh Yogarajah, Aniket Bharadwaj, Amelia Snook, Vashist Motkur
Cureus.2025;[Epub] CrossRef
Feasibility and reliability of the pandemic-adapted online-onsite hybrid graduation OSCE in Japan
Satoshi Hara, Kunio Ohta, Daisuke Aono, Toshikatsu Tamai, Makoto Kurachi, Kimikazu Sugimori, Hiroshi Mihara, Hiroshi Ichimura, Yasuhiko Yamamoto, Hideki Nomura
Advances in Health Sciences Education.2024; 29(3): 949. CrossRef
Should Virtual Objective Structured Clinical Examination (OSCE) Teaching Replace or Complement Face-to-Face Teaching in the Post-COVID-19 Educational Environment: An Evaluation of an Innovative National COVID-19 Teaching Programme
Charles Gamble, Alice Oatham, Raj Parikh
Cureus.2023;[Epub] CrossRef

Acceptability of the 8-case objective structured clinical examination of medical students in Korea using generalizability theory: a reliability study: Song Yi Park, Sang-Hwa Lee, Min-Jeong Kim, Ki-Hwan Ji, Ji Ho Ryu; J Educ Eval Health Prof. 2022;19:26. Published online September 8, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.26

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Abstract PDF Supplementary Material

Purpose
This study investigated whether the reliability was acceptable when the number of cases in the objective structured clinical examination (OSCE) decreased from 12 to 8 using generalizability theory (GT).
Methods
This psychometric study analyzed the OSCE data of 439 fourth-year medical students conducted in the Busan and Gyeongnam areas of South Korea from July 12 to 15, 2021. The generalizability study (G-study) considered 3 facets—students (p), cases (c), and items (i)—and designed the analysis as p×(i:c) due to items being nested in a case. The acceptable generalizability (G) coefficient was set to 0.70. The G-study and decision study (D-study) were performed using G String IV ver. 6.3.8 (Papawork, Hamilton, ON, Canada).
Results
All G coefficients except for July 14 (0.69) were above 0.70. The major sources of variance components (VCs) were items nested in cases (i:c), from 51.34% to 57.70%, and residual error (pi:c), from 39.55% to 43.26%. The proportion of VCs in cases was negligible, ranging from 0% to 2.03%.
Conclusion
The case numbers decreased in the 2021 Busan and Gyeongnam OSCE. However, the reliability was acceptable. In the D-study, reliability was maintained at 0.70 or higher if there were more than 21 items/case in 8 cases and more than 18 items/case in 9 cases. However, according to the G-study, increasing the number of items nested in cases rather than the number of cases could further improve reliability. The consortium needs to maintain a case bank with various items to implement a reliable blueprinting combination for the OSCE.

Citations

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From Agents to Governance: Essential AI Skills for Clinicians in the Large Language Model Era
Weiping Cao, Qing Zhang, Jialin Liu, Siru Liu
Journal of Medical Internet Research.2026; 28: e86550. CrossRef
Applying borderline methods for setting standards in objective structured clinical examination: the first case in undergraduate medical program in Vietnam
Hoa Thi Thu Doan, Hanh Thi My Nguyen, Anh Tuan Pham
MedPharmRes.2026; 10(1): 96. CrossRef
Applying the Generalizability Theory to Identify the Sources of Validity Evidence for the Quality of Communication Questionnaire
Flávia Del Castanhel, Fernanda R. Fonseca, Luciana Bonnassis Burg, Leonardo Maia Nogueira, Getúlio Rodrigues de Oliveira Filho, Suely Grosseman
American Journal of Hospice and Palliative Medicine®.2024; 41(7): 792. CrossRef

Medical students’ self-assessed efficacy and satisfaction with training on endotracheal intubation and central venous catheterization with smart glasses in Taiwan: a non-equivalent control-group pre- and post-test study: Yu-Fan Lin, Chien-Ying Wang, Yen-Hsun Huang, Sheng-Min Lin, Ying-Ying Yang; J Educ Eval Health Prof. 2022;19:25. Published online September 2, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.25

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Abstract PDF Supplementary Material

Purpose
Endotracheal intubation and central venous catheterization are essential procedures in clinical practice. Simulation-based technology such as smart glasses has been used to facilitate medical students’ training on these procedures. We investigated medical students’ self-assessed efficacy and satisfaction regarding the practice and training of these procedures with smart glasses in Taiwan.
Methods
This observational study enrolled 145 medical students in the 5th and 6th years participating in clerkships at Taipei Veterans General Hospital between October 2020 and December 2021. Students were divided into the smart glasses or the control group and received training at a workshop. The primary outcomes included students’ pre- and post-intervention scores for self-assessed efficacy and satisfaction with the training tool, instructor’s teaching, and the workshop.
Results
The pre-intervention scores for self-assessed efficacy of 5th- and 6th-year medical students in endotracheal intubation and central venous catheterization procedures showed no significant difference. The post-intervention score of self-assessed efficacy in the smart glasses group was better than that of the control group. Moreover, 6th-year medical students in the smart glasses group showed higher satisfaction with the training tool, instructor’s teaching, and workshop than those in the control group.
Conclusion
Smart glasses served as a suitable simulation tool for endotracheal intubation and central venous catheterization procedures training in medical students. Medical students practicing with smart glasses showed improved self-assessed efficacy and higher satisfaction with training, especially for procedural steps in a space-limited field. Simulation training on procedural skills with smart glasses in 5th-year medical students may be adjusted to improve their satisfaction.

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A narrative review of advancing medical education through technology: the role of smart glasses in situated learning
Bahareh Ghavami Hosein Pour, Zahra Karimian, Nazanin Hatami Niya
BMC Medical Education.2025;[Epub] CrossRef
Improvement of the Endotracheal Intubation Skill of Nurse Anesthesia Students Using Visual Self-evaluation in Iran: A Randomized Controlled Study
Mahdieh Parhizkar, Ali Khalafi, Masoumeh Albooghobeish, Nooshin Sarvi-Sarmeydani
Shiraz E-Medical Journal.2024;[Epub] CrossRef
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Brief report

Educational impact of an active learning session with 6-lead mobile electrocardiography on medical students’ knowledge of cardiovascular physiology during the COVID-19 pandemic in the United States: a survey-based observational study: Alexandra Camille Greb, Emma Altieri, Irene Masini, Emily Helena Frisch, Milton Leon Greenberg; J Educ Eval Health Prof. 2022;19:12. Published online June 20, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.12

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Abstract PDF Supplementary Material

Mobile electrocardiogram (ECG) devices are valuable tools for teaching ECG interpretation. The primary purpose of this follow-up study was to determine if an ECG active learning session could be safely and effectively performed during the coronavirus disease 2019 (COVID-19) pandemic using a newly developed mobile 6-lead ECG device. Additionally, we examined the educational impact of these active learning sessions on student knowledge of cardiovascular physiology and the utility of the mobile 6-lead ECG device in a classroom setting. In this study, first-year medical students (MS1) performed four active learning activities using the new mobile 6-lead ECG device. Data were collected from 42 MS1s through a quantitative survey administered in September 2020. Overall, students felt the activity enhanced their understanding of the course material and that the activity was performed safely and in compliance with local COVID-19 guidelines. These results emphasize student preference for hands-on, small group learning activities in spite of the pandemic.

Citations

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Medical student exam performance and perceptions of a COVID-19 pandemic-appropriate pre-clerkship medical physiology and pathophysiology curriculum
Melissa Chang, Andrew Cuyegkeng, Joseph A. Breuer, Arina Alexeeva, Abigail R. Archibald, Javier J. Lepe, Milton L. Greenberg
BMC Medical Education.2022;[Epub] CrossRef

Research articles

No difference in factual or conceptual recall comprehension for tablet, laptop, and handwritten note-taking by medical students in the United States: a survey-based observational study: Warren Wiechmann, Robert Edwards, Cheyenne Low, Alisa Wray, Megan Boysen-Osborn, Shannon Toohey; J Educ Eval Health Prof. 2022;19:8. Published online April 26, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.8

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Abstract PDF Supplementary Material

Purpose
Technological advances are changing how students approach learning. The traditional note-taking methods of longhand writing have been supplemented and replaced by tablets, smartphones, and laptop note-taking. It has been theorized that writing notes by hand requires more complex cognitive processes and may lead to better retention. However, few studies have investigated the use of tablet-based note-taking, which allows the incorporation of typing, drawing, highlights, and media. We therefore sought to confirm the hypothesis that tablet-based note-taking would lead to equivalent or better recall as compared to written note-taking.
Methods
We allocated 68 students into longhand, laptop, or tablet note-taking groups, and they watched and took notes on a presentation on which they were assessed for factual and conceptual recall. A second short distractor video was shown, followed by a 30-minute assessment at the University of California, Irvine campus, over a single day period in August 2018. Notes were analyzed for content, supplemental drawings, and other media sources.
Results
No significant difference was found in the factual or conceptual recall scores for tablet, laptop, and handwritten note-taking (P=0.61). The median word count was 131.5 for tablets, 121.0 for handwriting, and 297.0 for laptops (P=0.01). The tablet group had the highest presence of drawing, highlighting, and other media/tools.
Conclusion
In light of conflicting research regarding the best note-taking method, our study showed that longhand note-taking is not superior to tablet or laptop note-taking. This suggests students should be encouraged to pick the note-taking method that appeals most to them. In the future, traditional note-taking may be replaced or supplemented with digital technologies that provide similar efficacy with more convenience.

Citations

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The Power of the Pen in the Digital Age: A Study on the Note-Taking Preferences of Medical School Students
Ahmet Öksüz, Özlem Coşkun
STED / Sürekli Tıp Eğitimi Dergisi.2026; 35(2): 117. CrossRef
Evaluating the Use of a Note-Taking App by Japanese Resident Physicians: Nationwide Cross-Sectional Study
Taiju Miyagami, Yuji Nishizaki, Taro Shimizu, Yu Yamamoto, Kiyoshi Shikino, Koshi Kataoka, Masanori Nojima, Gautam A Deshpande, Toshio Naito, Yasuharu Tokuda
JMIR Formative Research.2025; 9: e55087. CrossRef
Cognitive-digital interaction: the state of the field, weaknesses and solutions
Anastasia Anufrieva
Acta Psychologica.2025; 259: 105229. CrossRef
A Classroom Study on Notetaking Modalities and Inattentive Attention‐Deficit/Hyperactivity Disorder Symptoms
Gabrielle A. Shimko, Emily R. Fyfe, Karin H. James
Applied Cognitive Psychology.2025;[Epub] CrossRef
Exploring the impact of note taking methods on cognitive function among university students
Alham Al-Sharman, Reime Jamal Shalash, Taif A. M. Omran, Rofaida Mohamed Elsayed, Ilhan Abdi Warfa, Wala Siddig Elsayed Ali Adawi, Amna Obaid Aljaberi, Alia Abdulla Alabdooli, Ashokan Arumugam, Sivapriya Ramakrishnan, Nabil Saad, Amal Ahbouch, Wegdan Bani
BMC Medical Education.2025;[Epub] CrossRef
Typed Versus Handwritten Lecture Notes and College Student Achievement: A Meta-Analysis
Abraham E. Flanigan, Jordan Wheeler, Tiphaine Colliot, Junrong Lu, Kenneth A. Kiewra
Educational Psychology Review.2024;[Epub] CrossRef

Comparing the cut score for the borderline group method and borderline regression method with norm-referenced standard setting in an objective structured clinical examination in medical school in Korea: Song Yi Park, Sang-Hwa Lee, Min-Jeong Kim, Ki-Hwan Ji, Ji Ho Ryu; J Educ Eval Health Prof. 2021;18:25. Published online September 27, 2021; DOI: https://doi.org/10.3352/jeehp.2021.18.25

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Abstract PDF Supplementary Material

Purpose
Setting standards is critical in health professions. However, appropriate standard setting methods do not always apply to the set cut score in performance assessment. The aim of this study was to compare the cut score when the standard setting is changed from the norm-referenced method to the borderline group method (BGM) and borderline regression method (BRM) in an objective structured clinical examination (OSCE) in medical school.
Methods
This was an explorative study to model the implementation of the BGM and BRM. A total of 107 fourth-year medical students attended the OSCE at 7 stations for encountering standardized patients (SPs) and at 1 station for performing skills on a manikin on July 15th, 2021. Thirty-two physician examiners evaluated the performance by completing a checklist and global rating scales.
Results
The cut score of the norm-referenced method was lower than that of the BGM (P<0.01) and BRM (P<0.02). There was no significant difference in the cut score between the BGM and BRM (P=0.40). The station with the highest standard deviation and the highest proportion of the borderline group showed the largest cut score difference in standard setting methods.
Conclusion
Prefixed cut scores by the norm-referenced method without considering station contents or examinee performance can vary due to station difficulty and content, affecting the appropriateness of standard setting decisions. If there is an adequate consensus on the criteria for the borderline group, standard setting with the BRM could be applied as a practical and defensible method to determine the cut score for OSCE.

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Refining competency benchmarks: a scoping review of Angoff standard-setting in dental education
Galvin Sim Siang Lin, Abdul Rauf Badrul Hisham, Muhammad Nazmi Abdul Majid, Chan Choong Foong, Ting Khee Ho, Lara T. Friedlander
BMC Oral Health.2026;[Epub] CrossRef
Standard setting methods in objective structured clinical examination (OSCE): A comparative study of five methods
Reshma Ansari, Norhafizah Ab Manan, Nur Ain Mahat, Norfaizatul Shalida Omar, Atikah Abdul Latiff, Sara Idris, Azli Shahril Othman
Journal of Medical Education Development.2024; 17(56): 87. CrossRef
Analyzing the Quality of Objective Structured Clinical Examination in Alborz University of Medical Sciences
Suleiman Ahmadi, Amin Habibi, Mitra Rahimzadeh, Shahla Bahrami
Alborz University Medical Journal.2023; 12(4): 485. CrossRef
Possibility of using the yes/no Angoff method as a substitute for the percent Angoff method for estimating the cutoff score of the Korean Medical Licensing Examination: a simulation study
Janghee Park
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Newly appointed medical faculty members’ self-evaluation of their educational roles at the Catholic University of Korea College of Medicine in 2020 and 2021: a cross-sectional survey-based study
Sun Kim, A Ra Cho, Chul Woon Chung
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Medical students’ pattern of self-directed learning prior to and during the coronavirus disease 2019 pandemic period and its implications for Free Open Access Meducation within the United Kingdom: Jack Barton, Kathrine Sofia Rallis, Amber Elyse Corrigan, Ella Hubbard, Antonia Round, Greta Portone, Ashvin Kuri, Tien Tran, Yu Zhi Phuah, Katie Knight, Jonathan Round; J Educ Eval Health Prof. 2021;18:5. Published online April 6, 2021; DOI: https://doi.org/10.3352/jeehp.2021.18.5

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Abstract PDF Supplementary Material

Purpose
Self-directed learning (SDL) has been increasingly emphasized within medical education. However, little is known about the SDL resources medical students use. This study aimed to identify patterns in medical students’ SDL behaviors, their SDL resource choices, factors motivating these choices, and the potential impact of the coronavirus disease 2019 (COVID-19) pandemic on these variables.
Methods
An online cross-sectional survey comprising multiple-choice, ranked, and free-text response questions were disseminated to medical students across all 41 UK medical schools between April and July 2020. Independent study hours and sources of study materials prior to and during the COVID-19 pandemic were compared. Motivational factors guiding resource choices and awareness of Free Open Access Meducation were also investigated.
Results
The target sample was 75 students per medical school across a total of 41 medical schools within the United Kingdom (3,075 total students), and 1,564 responses were analyzed. University-provided information comprised the most commonly used component of independent study time, but a minority of total independent study time. Independent study time increased as a result of the COVID-19 pandemic (P<0.001). All sub-cohorts except males reported a significant increase in the use of resources such as free websites and question banks (P<0.05) and paid websites (P<0.05) as a result of the pandemic. Accessibility was the most influential factor guiding resource choice (Friedman’s μrank=3.97, P<0.001).
Conclusion
The use of learning resources independent of university provision is increasing. Educators must ensure equitable access to such materials while supporting students in making informed choices regarding their independent study behaviors.

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Agreement between medical students’ peer assessments and faculty assessments in advanced resuscitation skills examinations in South Korea: Jinwoo Jeong, Song Yi Park, Kyung Hoon Sun; J Educ Eval Health Prof. 2021;18:4. Published online March 25, 2021; DOI: https://doi.org/10.3352/jeehp.2021.18.4

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Abstract PDF Supplementary Material: Purpose
In medical education, peer assessment is considered to be an effective learning strategy. Although several studies have examined agreement between peer and faculty assessments regarding basic life support (BLS), few studies have done so for advanced resuscitation skills (ARS) such as intubation and defibrillation. Therefore, this study aimed to determine the degree of agreement between medical students’ and faculty assessments of ARS examinations.
Methods
This retrospective explorative study was conducted during the emergency medicine (EM) clinical clerkship of fourth-year medical students from April to July 2020. A faculty assessor (FA) and a peer assessor (PA) assessed each examinee’s resuscitation skills (including BLS, intubation, and defibrillation) using a checklist that consisted of 20 binary items (performed or not performed) and 1 global proficiency rating using a 5-point Likert scale. The prior examinee assessed the next examinee after feedback and training as a PA. All 54 students participated in peer assessment. The assessments of 44 FA/PA pairs were analyzed using the intraclass correlation coefficient (ICC) and Gwet’s first-order agreement coefficient.
Results
The PA scores were higher than the FA scores (mean±standard deviation, 20.2±2.5 [FA] vs. 22.3±2.4 [PA]; P<0.001). The agreement was poor to moderate for the overall checklist (ICC, 0.55; 95% confidence interval [CI], 0.31 to 0.73; P<0.01), BLS (ICC, 0.19; 95% CI, -0.11 to 0.46; P<0.10), intubation (ICC, 0.51; 95% CI, 0.26 to 0.70; P<0.01), and defibrillation (ICC, 0.49; 95% CI, 0.23 to 0.68; P<0.01).
Conclusion
Senior medical students showed unreliable agreement in ARS assessments compared to faculty assessments. If a peer assessment is planned in skills education, comprehensive preparation and sufficient assessor training should be provided in advance.

Brief report

Core elements of character education essential for doctors suggested by medical students in Korea: a preliminary study: Yera Hur, Keumho Lee; J Educ Eval Health Prof. 2020;17:43. Published online December 21, 2020; DOI: https://doi.org/10.3352/jeehp.2020.17.43

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Abstract PDF Supplementary Material

This preliminary study aimed to determine how medical students perceive character education in Korea. A structured survey questionnaire was distributed to 10 medical students between September and December 2018, of whom 6 students replied. Students’ responses were classified into elements, which were also categorized. Twenty-nine core elements of characters in 8 categories were verified as essential for doctors and as needs for character education. The most frequently suggested categories were “care and respect,” “empathy and communication,” and “responsibility and calling.” Participants also stated that various forms of character education are necessary and that they were not satisfied with the teaching methods of the character education that they had received. These results verified the most essential character traits for doctors and identified problems related to current character education. The results of this study will be helpful for preparing the character education curriculum in medical schools.

Citations

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Character education empirical research: A thematic review and comparative content analysis
Peter Oldham, Shane McLoughlin
Journal of Moral Education.2026; 55(2): 313. CrossRef
Medical students’ self-evaluation of character, and method of character education
Yera Hur, Sanghee Yeo, Keumho Lee
BMC Medical Education.2022;[Epub] CrossRef
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Noor-i-Kiran Naeem, Zil-e-Fatima Naeem, Asfandyar Anwer
Journal of Shalamar Medical & Dental College - JSHMDC.2022; 3(2): 48. CrossRef
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Research articles

Female medical and nursing students’ knowledge, attitudes, and skills regarding breast self-examination in Oman: a comparison between pre- and post-training: Rajani Ranganath, John Muthusami, Miriam Simon, Tatiyana Mandal, Meena Anand Kukkamulla; J Educ Eval Health Prof. 2020;17:37. Published online December 1, 2020; DOI: https://doi.org/10.3352/jeehp.2020.17.37

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Purpose
Breast cancer is one of the most common cancers in women worldwide. Educational and awareness programs impact early practices of breast self-examination, resulting in the early detection of cancer and thereby decreasing mortality. The study aimed to assess the levels of knowledge and awareness of breast cancer and breast self-examination among medical and nursing students in Oman and to compare their knowledge, attitudes, and skills after a training program.
Methods
This quasi-experimental study was carried out for female 90 medical and 80 nursing students in Oman in November 2019. A pre-test questionnaire was given before the training program and a post-test questionnaire was administered after the training program. Students’ knowledge, attitude, and skills regarding breast cancer and breast self-examination were compared. Scores for skills of practicing breast self-examination were compared between lecture and activity group and lecture-only group.
Results
Pre-test and post-test data were collected from 170 female students. Significant improvements were observed in the post-test scores for students’ knowledge, attitude, and skills after the intervention (P<0.001). The mean scores for skills of practicing breast self-examination after the lecture and the activity were higher than those obtained after the lecture only (P=0.014 for medical students and P=0.016 for nursing students).
Conclusion
An educational training program on breast cancer and breast examination with an emphasis on skills can motivate participants to perform breast self-examination regularly, and may therefore help students to train other women to perform breast self-examination for the early detection of breast cancer.

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Effectiveness of face to face and virtual education to promote breast self-examination based on the theory of planned behavior: a randomized controlled trial study
Halime Cheraghalizadeh, Hajar Adib-Rad, Hajar Pasha, Mohammad Chehrazi, Fatemeh Nasiri‑Amiri, Shabnam Omidvar
BMC Cancer.2025;[Epub] CrossRef
Predicting breast self-examination awareness in Sub-Saharan Africa using machine learning
Nebebe Demis Baykemagn, Meron Asmamaw Alemayehu, Tirualem Zeleke Yehuala, Agmasie Damtew Walle, Andualem Enyew Gedefaw, Abraham Keffale Mengistu
Scientific Reports.2025;[Epub] CrossRef
Breast Self-Examination: Evaluating Knowledge, Attitudes, and Practices Among Female Medical Students
Shruti Raghavan, Shraddha Mishra, Abhijit Das, Sandhya Singh
Cureus.2025;[Epub] CrossRef
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Breast self-examination among female medical students at Damascus University: A cross-sectional study
Mohammed Alshafie, Anas Bitar, Massa Alfawal, Mhd Basheer Alameer, Dima Alhomsi, Maher Saifo
Heliyon.2024; 10(15): e35312. CrossRef
Kavram Haritası ile Verilen Kendi Kendine Meme Muayenesi Eğitiminin Hemşirelik Öğrencilerinin Sağlık İnançları ve Öz Yeterlilik Düzeylerine Etkisi
Aysun Acun, Yadigar Ordu
Black Sea Journal of Health Science.2023; 6(4): 632. CrossRef
Effectiveness of Online Peer-Assisted Learning Session in Fostering the Knowledge on Breast Cancer and Breast Self-Examination among Undergraduate Medical Students
R Ranganath, MA Simon, YA Shah, FI AlAbduwani, H Al Mubarak, FA Al-Shamsi
Journal of Nature and Science of Medicine.2023; 6(2): 71. CrossRef

Training in statistical analysis reduces the framing effect among medical students and residents in Argentina: Raúl Alfredo Borracci, Eduardo Benigno Arribalzaga, Jorge Thierer; J Educ Eval Health Prof. 2020;17:25. Published online September 1, 2020; DOI: https://doi.org/10.3352/jeehp.2020.17.25

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Abstract PDF Supplementary Material

Purpose
The framing effect refers to a phenomenon wherein, when the same problem is presented using different representations of information, people make significant changes in their decisions. This study aimed to explore whether the framing effect could be reduced in medical students and residents by teaching them the statistical concepts of effect size, probability, and sampling for use in the medical decision-making process.
Methods
Ninety-five second-year medical students and 100 second-year medical residents of Austral University and Buenos Aires University, Argentina were invited to participate in the study between March and June 2017. A questionnaire was developed to assess the different types of framing effects in medical situations. After an initial administration of the survey, students and residents were taught statistical concepts including effect size, probability, and sampling during 2 individual independent official biostatistics courses. After these interventions, the same questionnaire was randomly administered again, and pre- and post-intervention outcomes were compared among students and residents.
Results
Almost every type of framing effect was reproduced either in the students or in the residents. After teaching medical students and residents the analytical process behind statistical concepts, a significant reduction in sample-size, risky-choice, pseudo-certainty, number-size, attribute, goal, and probabilistic formulation framing effects was observed.
Conclusion
The decision-making of medical students and residents in simulated medical situations may be affected by different frame descriptions, and these framing effects can be partially reduced by training individuals in probability analysis and statistical sampling methods.

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Brief Report

Potential of feedback during objective structured clinical examination to evoke an emotional response in medical students in Canada: Dalia Limor Karol, Debra Pugh; J Educ Eval Health Prof. 2020;17:5. Published online February 18, 2020; DOI: https://doi.org/10.3352/jeehp.2020.17.5

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Abstract PDF Supplementary Material

Feedback has been shown to be an important driver for learning. However, many factors, such as the emotional reactions feedback evokes, may impact its effect. This study aimed to explore medical students’ perspectives on the verbal feedback they receive during an objective structured clinical examination (OSCE); their emotional reaction to this; and its impact on their subsequent performance. To do this, medical students enrolled at 4 Canadian medical schools were invited to complete a web-based survey regarding their experiences. One hundred and fifty-eight participants completed the survey. Twenty-nine percent of respondents asserted that they had experienced emotional reactions to verbal feedback received in an OSCE setting. The most common emotional responses reported were embarrassment and anxiousness. Some students (n=20) reported that the feedback they received negatively impacted subsequent OSCE performance. This study demonstrates that feedback provided during an OSCE can evoke an emotional response in students and potentially impact subsequent performance.

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JYOTSNA AGARWAL, VIKRAMJEET SINGH, MANISH KUMAR SINGH, THOMAS V. CHACKO
The National Medical Journal of India.2026; 39: 108. CrossRef
Faculty Perceptions of Feedback in Objective Structured Clinical Examinations (OSCEs)
Zainab Abdullah, Lubna Kashif, Marina Khan, Syeda Sanaa Fatima, Asya Tauqir, Saima Manzoor
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Memory, credibility and insight: How video-based feedback promotes deeper reflection and learning in objective structured clinical exams
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Research articles

Impact of a narrative medicine program on reflective capacity and empathy of medical students in Iran: Saeideh Daryazadeh, Payman Adibi, Nikoo Yamani, Roya Mollabashi; J Educ Eval Health Prof. 2020;17:3. Published online January 27, 2020; DOI: https://doi.org/10.3352/jeehp.2020.17.3

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Abstract PDF Supplementary Material

Purpose
Narrative medicine consists of the expression of medical experiences and the reflection on narratives to foster empathic communication with patients. Reflecting on narratives increases self-awareness and recognition of the feelings of the narrator or the story’s main character, which in turn affects the audience. This study was conducted to examine the impact of a narrative medicine program on the reflective capacity and empathy of medical students.
Methods
A quasi-experimental study was performed during the 2018–2019 academic year at Isfahan University of Medical Sciences in Iran involving 135 medical interns in 2 groups (control [n=66] and experimental [n=69]). Interns in the experimental group took part in seven 2-hour reflective practice sessions, while those in the control group underwent no educational intervention. Pre-test and post-test assessments were conducted for both groups using 2 valid and reliable tools for the assessment of reflective capacity and empathy. Mean reflection and empathy scores were compared within groups (between pre- and post-test values) and between groups (using the paired-t test and the t-test; P≤0.05).
Results
The mean reflection and empathy scores of the experimental group significantly increased from pre-test to post-test, but those of the control group did not. Moreover, the mean post-test scores were significantly different between the 2 groups (P<0.001).
Conclusion
Narrative medicine is an effective teaching method that can improve reflective capacity and empathy, thereby ultimately promoting professionalism as a core competency in medicine. Consideration of learning conditions and interdisciplinary teaching are necessary for implementing a narrative medicine program.

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International Journal of Academic Medicine.2020; 6(2): 121. CrossRef

Dreyfus scale-based feedback increased medical students’ satisfaction with the complex cluster part of a interviewing and physical examination course and improved skills readiness in Taiwan: Shiau-Shian Huang, Chia-Chang Huang, Ying-Ying Yang, Shuu-Jiun Wang, Boaz Shulruf, Chen-Huan Chen; J Educ Eval Health Prof. 2019;16:30. Published online October 11, 2019; DOI: https://doi.org/10.3352/jeehp.2019.16.30

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Abstract PDF Supplementary Material: Purpose
In contrast to the core part of the clinical interviewing and physical examination (PE) skills course, corresponding to the basic, head-to-toe, and thoracic systems, learners need structured feedback in the cluster part of the course, which includes the abdominal, neuromuscular, and musculoskeletal systems. This study evaluated the effects of using Dreyfus scale-based feedback, which has elements of continuous professional development, instead of Likert scale-based feedback in the cluster part of training in Taiwan.
Methods
Instructors and final-year medical students in the 2015–2016 classes of National Yang-Ming University, Taiwan comprised the regular cohort, whereas those in the 2017–2018 classes formed the intervention cohort. In the intervention cohort, Dreyfus scale-based feedback, rather than Likert scale-based feedback, was used in the cluster part of the course.
Results
In the cluster part of the course in the regular cohort, pre-trained standardized patients rated the class climate as poor, and students expressed low satisfaction with the instructors and course and low self-assessed readiness. In comparison with the regular cohort, improved end-of-course group objective structured clinical examination scores after the cluster part were noted in the intervention cohort. In other words, the implementation of Dreyfus scale-based feedback in the intervention cohort for the cluster part improved the deficit in this section of the course.
Conclusion
The implementation of Dreyfus scale-based feedback helped instructors to create a good class climate in the cluster part of the clinical interviewing and PE skills course. Simultaneously, this new intervention achieved the goal of promoting medical students’ readiness for interviewing, PE, and self-directed learning.

Peer-assisted feedback: a successful approach for providing feedback on United States Medical Licensing Exam-style clinical skills exam notes in the United States: Kira Nagoshi, Zareen Zaidi, Ashleigh Wright, Carolyn Stalvey; J Educ Eval Health Prof. 2019;16:29. Published online October 8, 2019; DOI: https://doi.org/10.3352/jeehp.2019.16.29

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Abstract PDF Supplementary Material

Purpose
Peer-assisted learning (PAL) promotes the development of communication, facilitates improvements in clinical skills, and is a way to provide feedback to learners. We utilized PAL as a conceptual framework to explore the feasibility of peer-assisted feedback (PAF) to improve note-writing skills without requiring faculty time. The aim was to assess whether PAL was a successful method to provide feedback on the United States Medical Licensing Exams (USMLE)-style clinical skills exam notes by using student feedback on a survey in the United States.
Methods
The University of Florida College of Medicine administers clinical skills examination (CSEs) that include USMLE-like note-writing. PAL, in which students support the learning of their peers, was utilized as an alternative to faculty feedback. Second-year (MS2) and third-year (MS3) medical students taking CSEs participated in faculty-run note-grading sessions immediately after testing, which included explanations of grading rubrics and the feedback process. Students graded an anonymized peer’s notes. The graded material was then forwarded anonymously to its student author to review. Students were surveyed on their perceived ability to provide feedback and the benefits derived from PAF using a Likert scale (1–6) and open-ended comments during the 2017–2018 academic year.
Results
Students felt generally positively about the activity, with mean scores for items related to educational value of 4.49 for MS2s and 5.11 for MS3s (out of 6). MS3s perceived peer feedback as constructive, felt that evaluating each other’s notes was beneficial, and felt that the exercise would improve their future notes. While still positive, MS2 students gave lower scores than the MS3 students.
Conclusion
PAF was a successful method of providing feedback on student CSE notes, especially for MS3s. MS2s commented that although they learned during the process, they might be more invested in improving their note-writing as they approach their own USMLE exam.

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Review

What should medical students know about artificial intelligence in medicine?: Seong Ho Park, Kyung-Hyun Do, Sungwon Kim, Joo Hyun Park, Young-Suk Lim; J Educ Eval Health Prof. 2019;16:18. Published online July 3, 2019; DOI: https://doi.org/10.3352/jeehp.2019.16.18

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Abstract PDF Supplementary Material

Artificial intelligence (AI) is expected to affect various fields of medicine substantially and has the potential to improve many aspects of healthcare. However, AI has been creating much hype, too. In applying AI technology to patients, medical professionals should be able to resolve any anxiety, confusion, and questions that patients and the public may have. Also, they are responsible for ensuring that AI becomes a technology beneficial for patient care. These make the acquisition of sound knowledge and experience about AI a task of high importance for medical students. Preparing for AI does not merely mean learning information technology such as computer programming. One should acquire sufficient knowledge of basic and clinical medicines, data science, biostatistics, and evidence-based medicine. As a medical student, one should not passively accept stories related to AI in medicine in the media and on the Internet. Medical students should try to develop abilities to distinguish correct information from hype and spin and even capabilities to create thoroughly validated, trustworthy information for patients and the public.

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JMIR Medical Education.2024; 10: e54793. CrossRef
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Cureus.2024;[Epub] CrossRef
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Medical Physics.2023; 50(3): 1528. CrossRef
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Journal of the American Medical Informatics Association.2023; 30(3): 604. CrossRef
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Journal of Educational Evaluation for Health Professions.2023; 20: 1. CrossRef
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Humairah Zainal, Xin Xiaohui, Julian Thumboo, Fong Kok Yong, Conor Gilligan
PLOS ONE.2023; 18(3): e0281108. CrossRef
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JMIR Medical Education.2023; 9: e46428. CrossRef
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Healthcare.2023; 11(9): 1298. CrossRef
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Zuhal Y. Hamd, Wiam Elshami, Sausan Al Kawas, Hanan Aljuaid, Mohamed M. Abuzaid
Heliyon.2023; 9(6): e17089. CrossRef
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Healthcare.2023; 11(13): 1812. CrossRef
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Cureus.2023;[Epub] CrossRef
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Hyeonmi Hong, Youngjoon Kang, Youngjon Kim, Bomsol Kim
Journal of Medicine and Life Science.2023; 20(2): 53. CrossRef
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Current Medical Imaging Formerly Current Medical Imaging Reviews.2023;[Epub] CrossRef
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BMC Medical Education.2023;[Epub] CrossRef
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World Neurosurgery.2023; 180: e142. CrossRef
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Ahmed Marey, Abdelrahman M Saad, Benjamin D Killeen, Catalina Gomez, Mariia Tregubova, Mathias Unberath, Muhammad Umair
BJR|Open.2023;[Epub] CrossRef
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Radiología.2022; 64(6): 516. CrossRef
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Davide Luciani, Alessandro Magrini, Carlo Berzuini, Antonello Gavazzi, Paolo Canova, Tiziano Barbui, Guido Bertolini
Expert Systems with Applications.2022; 189: 116066. CrossRef
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Haytham Siala, Yichuan Wang
Social Science & Medicine.2022; 296: 114782. CrossRef
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INDIAN JOURNAL OF APPLIED RESEARCH.2022; : 46. CrossRef
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Pediatric and Developmental Pathology.2022; 25(4): 380. CrossRef
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JMIR Medical Education.2022; 8(2): e35223. CrossRef
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Joel Grunhut, Oge Marques, Adam T M Wyatt
JMIR Medical Education.2022; 8(2): e35587. CrossRef
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Frontiers in Public Health.2022;[Epub] CrossRef
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Currents in Pharmacy Teaching and Learning.2022; 14(7): 854. CrossRef
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Advances in Medical Education and Practice.2022; Volume 13: 927. CrossRef
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JMIR Medical Education.2022; 8(4): e38325. CrossRef
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BMC Medical Education.2022;[Epub] CrossRef
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Radiología (English Edition).2022; 64(6): 516. CrossRef
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Cureus.2022;[Epub] CrossRef
AI in the hands of imperfect users
Kristin M. Kostick-Quenet, Sara Gerke
npj Digital Medicine.2022;[Epub] CrossRef
Trust and medical AI: the challenges we face and the expertise needed to overcome them
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Journal of the American Medical Informatics Association.2021; 28(4): 890. CrossRef
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Luigi Manco, Nicola Maffei, Silvia Strolin, Sara Vichi, Luca Bottazzi, Lidia Strigari
Physica Medica.2021; 83: 194. CrossRef
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Actas Urológicas Españolas.2021; 45(8): 524. CrossRef
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Hastings Center Report.2021; 51(5): 6. CrossRef
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Elena A Wood, Brittany L Ange, D Douglas Miller
Journal of Medical Education and Curricular Development.2021;[Epub] CrossRef
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Brief reports

No observed effect of a student-led mock objective structured clinical examination on subsequent performance scores in medical students in Canada: Lorenzo Madrazo, Claire Bo Lee, Meghan McConnell, Karima Khamisa, Debra Pugh; J Educ Eval Health Prof. 2019;16:14. Published online May 27, 2019; DOI: https://doi.org/10.3352/jeehp.2019.16.14

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Student-led peer-assisted mock objective structured clinical examinations (MOSCEs) have been used in various settings to help students prepare for subsequent higher-stakes, faculty-run OSCEs. MOSCE participants generally valued feedback from peers and reported benefits to learning. Our study investigated whether participation in a peer-assisted MOSCE affected subsequent OSCE performance. To determine whether mean OSCE scores differed depending on whether medical students participated in the MOSCE, we conducted a between-subjects analysis of variance, with cohort (2016 vs. 2017) and MOSCE participation (MOSCE vs. no MOSCE) as independent variables and the mean OSCE score as the dependent variable. Participation in the MOSCE had no influence on mean OSCE scores (P=0.19). There was a significant correlation between mean MOSCE scores and mean OSCE scores (Pearson r=0.52, P<0.001). Although previous studies described self-reported benefits from participation in student-led MOSCEs, it was not associated with objective benefits in this study.

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Leonardo Mateus de Lima, Maria Helena Favarato, Iolanda Fátima Lopes Calvo Tibério, Lorenzo Faggioni
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MEDTalks: a student-driven program to enhance undergraduate student understanding and interest in medical schools in Canada: Jayson Azzi, Dalia Karol, Tayler Bailey, Christopher Jerome Ramnanan; J Educ Eval Health Prof. 2019;16:13. Published online May 22, 2019; DOI: https://doi.org/10.3352/jeehp.2019.16.13

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Given the lack of programs geared towards educating undergraduate students about medical school, the purpose of this study was to evaluate whether a medical student–driven initiative program, MEDTalks, enhanced undergraduate students’ understanding of medical school in Canada and stimulated their interest in pursuing medicine. The MEDTalks program, which ran between January and April 2018 at the University of Ottawa, consisted of 5 teaching sessions, each including large-group lectures, small-group case-based learning, physical skills tutorials, and anatomy lab demonstrations, to mimic the typical medical school curriculum. At the end of the program, undergraduate student learners were invited to complete a feedback questionnaire. Twenty-nine participants provided feedback, of whom 25 reported that MEDTalks allowed them to gain exposure to the University of Ottawa medical program; 27 said that it gave them a greater understanding of the teaching structure; and 25 responded that it increased their interest in attending medical school. The MEDTalks program successfully developed a greater understanding of medical school and helped stimulate interest in pursuing medical studies among undergraduate students.

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Research articles

Factors influencing the career preferences of medical students and interns: a cross-sectional, questionnaire-based survey from India: Ruban Anand, Prakash Somi Sankaran; J Educ Eval Health Prof. 2019;16:12. Published online May 15, 2019; DOI: https://doi.org/10.3352/jeehp.2019.16.12

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Purpose
The study aimed to identify the motivational factors and demographic variables influencing the career preferences of medical students in India.
Methods
We conducted a questionnaire-based survey at Christian Medical College, Vellore, India. The participants were 368 of the 460 medical students and interns enrolled at the institution from October 2015 to August 2016. We designed the questionnaire to collect demographic data, students’ preferences for career specialties, and the motivational factors influencing them. Then, we analyzed the influence of these factors and demographic variables on career preferences using regression analysis.
Results
Of the 368 respondents, 356 (96.7%) expressed their intention to pursue a residency program after the Bachelor of Medicine and Bachelor of Surgery (MBBS) program, and about two-thirds indicated their preference to do so in India. The specialties most preferred by students were general surgery, general medicine (internal medicine), and pediatrics, while the least preferred were anatomy, obstetrics and gynecology, and community medicine. Factor analysis yielded three motivational factors, which we named ‘personal growth,’ ‘professional growth,’ and ‘personal satisfaction’ based on the items loaded in each. The motivational factors were predicted by demographic variables (gender, geographical background, current stage in the MBBS program, and the presence of relatives in the health professions). Demographic variables and the motivational factors also had significant influences on career preferences.
Conclusion
This study provides insights into the motivational factors that influence the career preferences of Indian medical students and interns. A robust longitudinal study would be required to study intra-individual variations in preferences and the persistence of choices.

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Comparison of the effects of simulated patient clinical skill training and student roleplay on objective structured clinical examination performance among medical students in Australia: Silas Taylor, Matthew Haywood, Boaz Shulruf; J Educ Eval Health Prof. 2019;16:3. Published online January 11, 2019; DOI: https://doi.org/10.3352/jeehp.2019.16.3

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Abstract PDF Supplementary Material

Purpose
Optimal methods for communication skills training (CST) are an active research area, but the effects of CST on communication performance in objective structured clinical examinations (OSCEs) has not been closely studied. Student roleplay (RP) for CST is common, although volunteer simulated patient (SP) CST is cost-effective and provides authentic interactions. We assessed whether our volunteer SP CST program improved OSCE performance compared to our previous RP strategy.
Methods
We performed a retrospective, quasi-experimental study of 2 second-year medical student cohorts’ OSCE data in Australia. The 2014 cohort received RP-only CST (N=182) while the 2016 cohort received SP-only CST (N=148). The t-test and analysis of variance were used to compare the total scores in 3 assessment domains: generic communication, clinical communication, and physical examination/procedural skills.
Results
The baseline characteristics of groups (scores on the Australian Tertiary Admission Rank, Undergraduate Medicine and Health Sciences Admission Test, and medicine program interviews) showed no significant differences between groups. For each domain, the SP-only CST group demonstrated superior OSCE outcomes, and the difference between cohorts was significant (P<0.01). The superiority of volunteer SP CST over student RP CST in terms of OSCE performance outcomes was found for generic communication, clinical communication, and physical examination/procedural skills.
Conclusion
The better performance of the SP cohort in physical examination/procedural skills might be explained by the requirement for patient compliance and cooperation, facilitated by good generic communication skills. We recommend a volunteer SP program as an effective and efficient way to improve CST among junior medical students.

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Case reports

Distribution and academic significance of learning approaches among pre-clinical medical students at Trinity School of Medicine, St Vincent and the Grenadines: Keshab Raj Paudel, Hari Prasad Nepal, Binu Shrestha, Raju Panta, Stephen Toth; J Educ Eval Health Prof. 2018;15:9. Published online April 6, 2018; DOI: https://doi.org/10.3352/jeehp.2018.15.9

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Purpose
Different students may adopt different learning approaches: namely, deep and surface. This study aimed to characterize the learning strategies of medical students at Trinity School of Medicine and to explore potential correlations between deep learning approach and the students’ academic scores.
Methods
The study was a questionnaire-based, cross-sectional, observational study. A total of 169 medical students in the basic science years of training were included in the study after giving informed consent. The Biggs’s Revised Two-Factor Study Process Questionnaire in paper form was distributed to subjects from January to November 2017. For statistical analyses, the Student t-test, 1-way analysis of variance followed by the post-hoc t-test, and the Pearson correlation test were used. The Cronbach alpha was used to test the internal consistency of the questionnaire.
Results
Of the 169 subjects, 132 (response rate, 78.1%) completely filled out the questionnaires. The Cronbach alpha value for the items on the questionnaire was 0.8. The score for the deep learning approach was 29.4± 4.6, whereas the score for the surface approach was 24.3± 4.2, which was a significant difference (P< 0.05). A positive correlation was found between the deep learning approach and students’ academic performance (r= 0.197, P< 0.05, df= 130).
Conclusion
Medical students in the basic science years at Trinity School of Medicine adopted the deep learning approach more than the surface approach. Likewise, students who were more inclined towards the deep learning approach scored significantly higher on academic tests.

Citations

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Evaluating the Dynamics of Learning Approaches: A Systematic Review Investigating the Nexus Between Teaching Methods and Academic Performance in Medical and Dental Education
Marlen A. Roehe, Carmen Trost, Julia S. Grundnig, Anahit Anvari-Pirsch, Anita Holzinger
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Predictors of undergraduate occupational therapy students’ academic performance during the Covid-19 pandemic: A hierarchical regression analysis
Ted Brown, Luke Robinson, Kate Gledhill, Mong-Lin Yu, Stephen Isbel, Craig Greber, Dave Parsons, Jamie Etherington
Scandinavian Journal of Occupational Therapy.2023; 30(4): 475. CrossRef
Student characteristics associated with dominant approaches to studying: Comparing a national and an international sample
Mikkel M. Thørrisen, Gry Mørk, Lene A. Åsli, Astrid Gramstad, Linda Stigen, Trine A. Magne, Tove Carstensen, Susanne G. Johnson, Ted Brown, Hua B. Lim, Kenneth N. K. Fong, Tore Bonsaksen
Scandinavian Journal of Occupational Therapy.2022; 29(1): 13. CrossRef
Development and Preliminary Validation of the Physical Education-Study Process Questionnaire : Insights for Physical Education University Students
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Authenticity, acceptability, and feasibility of a hybrid gynecology station for the Papanicolaou test as part of a clinical skills examination in Korea: Ji-Hyun Seo, Younglim Oh, Sunju Im, Do-Kyong Kim, Hyun-Hee Kong, HyeRin Roh; J Educ Eval Health Prof. 2018;15:4. Published online February 13, 2018; DOI: https://doi.org/10.3352/jeehp.2018.15.4

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Abstract PDF Supplementary Material

Purpose
The objective of this study was to evaluate the authenticity, acceptability, and feasibility of a hybrid station that combined a standardized patient encounter and a simulated Papanicolaou test.
Methods
We introduced a hybrid station in the routine clinical skills examination (CSE) for 335 third-year medical students at 4 universities in Korea from December 1 to December 3, 2014. After the tests, we conducted an anonymous survey on the authenticity, acceptability, and feasibility of the hybrid station.
Results
A total of 334 medical students and 17 professors completed the survey. A majority of the students (71.6%) and professors (82.4%) agreed that the hybrid station was more authentic than the standard CSE. Over 60 percent of the students and professors responded that the station was acceptable for assessing the students’ competence. Most of the students (75.2%) and professors (82.4%) assessed the required tasks as being feasible after reading the instructions.
Conclusion
Our results showed that the hybrid CSE station was a highly authentic, acceptable, and feasible way to assess medical students’ performance.

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Research article

Clinical empathy in medical students in India measured using the Jefferson Scale of Empathy–Student Version: Anirban Chatterjee, Rajkrishna Ravikumar, Satendra Singh, Pranjal Singh Chauhan, Manu Goel; J Educ Eval Health Prof. 2017;14:33. Published online December 27, 2017; DOI: https://doi.org/10.3352/jeehp.2017.14.33

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Purpose
The purpose of this study was to assess the clinical empathy of a cohort of medical students spanning 4 years of undergraduate study and to identify factors associated with empathy.
Methods
A cross-sectional study to assess the empathy of undergraduate medical students at the University College of Medical Sciences and GTB Hospital in Delhi, India, was conducted using the Jefferson Scale of Empathy–Student Version. Demographic data were obtained using a pre-tested, semi-open-ended questionnaire.
Results
Of the 600 students, 418 participated in the survey (69.7%). The mean empathy score was 96.01 (of a maximum of 140), with a standard deviation of 14.56. The empathy scores decreased from the first to the third semester, plateaued at the fifth semester, and rose again in the seventh semester. Empathy was found to be significantly associated with the gender of the participant, with females having higher scores (P<0.001). The age of the participant, place of residence, whose decision it was for the student to enroll in an MBBS (bachelor of medicine and bachelor of surgery) program, and the choice of future specialty were not significantly associated with students’ empathy scores.
Conclusion
The study found significant gender differences in empathy among the participants. The empathy scores tended to decline initially and then rebound over time. The mean empathy levels found in this study are lower than those reported in most similar studies around the world; therefore, further studies are needed to analyze and address the underlying factors associated with this discrepancy.

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Advances in Health Sciences Education.2024; 29(4): 1131. CrossRef
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Reliability of Greek version of the Toronto empathy questionnaire in medical students and associations with sociodemographic and lifestyle factors
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Brief report

Smart device-based testing for medical students in Korea: satisfaction, convenience, and advantages: Eun Young Lim, Mi Kyoung Yim, Sun Huh; J Educ Eval Health Prof. 2017;14:7. Published online April 24, 2017; DOI: https://doi.org/10.3352/jeehp.2017.14.7

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Abstract PDF

The aim of this study was to investigate respondents’ satisfaction with smart device-based testing (SBT), as well as its convenience and advantages, in order to improve its implementation. The survey was conducted among 108 junior medical students at Kyungpook National University School of Medicine, Korea, who took a practice licensing examination using SBT in September 2015. The survey contained 28 items scored using a 5-point Likert scale. The items were divided into the following three categories: satisfaction with SBT administration, convenience of SBT features, and advantages of SBT compared to paper-and-pencil testing or computer-based testing. The reliability of the survey was 0.95. Of the three categories, the convenience of the SBT features received the highest mean (M) score (M= 3.75, standard deviation [SD]= 0.69), while the category of satisfaction with SBT received the lowest (M= 3.13, SD= 1.07). No statistically significant differences across these categories with respect to sex, age, or experience were observed. These results indicate that SBT was practical and effective to take and to administer.

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Brief Reports

Medical students’ perception of the proposal for theme-based integrated multi-disciplinary objective structured practical examination in Saudi Arabia: Mohammad Saleh Hassan, Amel Yacoubi; J Educ Eval Health Prof. 2016;13:15. Published online March 31, 2016; DOI: https://doi.org/10.3352/jeehp.2016.13.15

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Abstract PDF

This study aimed to find the opinion of preclinical medical students concerning a new suggested approach for practical assessment. Fifty-three female students agreed to participate in this study, out of 87 registered students in years 2 and 3 of the basic science phase of the College of Medicine, Qassim University, Kingdom of Saudi Arabia. Full explanation was made to the students of theme-based integrated objective structured practical examination (TBI-OSPE), followed by distribution of a questionnaire to collect the students’ opinions. The study was conducted in January 2015. Results showed that 78% of respondents were accepting of this new approach, and that only 5.7% rejected it. This difference was statistically significant (P<0.05). This study suggested a new model for assessment of preclinical students’ competencies using the proposed tool (TBI-OSPE) rather than standard classical OSPE, particularly in curricula involving high levels of integration and theme-based problems. This form of assessment would more positively enhance learning.

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Fahad Abdulaziz Alrashed, Tauseef Ahmad, Abdulrahman M. Alsubiheen, Saad A. Alhammad, Mishal M. Aldaihan, Alaa M. Albishi, Zafrul Hasan
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Pre-clinical versus clinical medical students’ attitudes towards the poor in the United States: Danial Jilani, Ashley Fernandes, Nicole Borges; J Educ Eval Health Prof. 2015;12:52. Published online November 1, 2015; DOI: https://doi.org/10.3352/jeehp.2015.12.52

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This study assessed the poverty-related attitudes of pre-clinical medical students (first and second years) versus clinical medical students (third and fourth years). First through fourth year medical students voluntarily completed the Attitude Towards Poverty scale. First and second year students were classified together in the preclinical group and third and fourth year students together in the clinical group. A total of 297 students participated (67% response rate). Statistically significant differences were noted between pre-clinical and clinical students for scores on the subscales personal deficiency (P<0.001), stigma (P=0.023), and for total scores (P=0.016). Scores across these subscales and for total scores were all higher in the clinical group. The only subscale which did not show statistical significance between pre-clinical and clinical students was the structural perspective. Medical students in their clinical training have a less favorable attitude towards the poor than their preclinical counterparts.

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Medical students attitudes toward and intention to work with the underserved: a systematic review and meta-analysis
Edouard Leaune, Violette Rey-Cadilhac, Safwan Oufker, Stéphanie Grot, Roy Strowd, Gilles Rode, Sonia Crandall
BMC Medical Education.2021;[Epub] CrossRef
Medical Students’ Perceptions of and Responses to Health Care Disparities During Clinical Clerkships
Johanna Glaser, Alana Pfeffinger, Judy Quan, Alicia Fernandez
Academic Medicine.2019; 94(8): 1190. CrossRef

Moroccan medical students’ perceptions of their educational environment: Jihane Belayachi, Rachid Razine, Amina Boufars, Asma Saadi, Naoufal Madani, Souad Chaouir, Redouane Abouqal; J Educ Eval Health Prof. 2015;12:47. Published online October 28, 2015; DOI: https://doi.org/10.3352/jeehp.2015.12.47

30,080 View
170 Download
9 Web of Science
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This study aimed to assess students’ perceptions of their educational environment in the Faculty of Medicine and Pharmacy of Rabat, Morocco, using the Dundee Ready Educational Environment Measure (DREEM). A cross-sectional survey was conducted in the Faculty of Medicine and Pharmacy of Rabat, Morocco, in which medical students’ perceptions of their educational environment were assessed using the DREEM criteria during the 2013-2014 academic years. The DREEM inventory encompasses 50 items divided into five subdomains: perceptions of learning, perceptions of teaching, academic self-perceptions, perceptions of atmosphere, and social self-perceptions. The DREEM has a maximum score of 200, which would correspond to a perfect educational environment. The mean scores (±standard deviation) of students’ responses were compared according to their year of study and gender. The responses of 189 postgraduate medical students were included. The mean total DREEM score was 90.8 (45.4%). The mean total scores for five subdomains were 21.2/48 (44.2%), 21.8/44 (49.6%), 13.1/32 (40.9%), 19.0/48 (39.6%), and 15.6/28 (55.7%) respectively. Female students reported higher perceptions of teaching scores than males (P=0.002), and students in their fifth year of study reported significantly higher social self-perceptions scores than those in their fourth year (P=0.03). In this study of the oldest faculty of medicine in Morocco, students perceived the educational environment as having many problems.

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Psychometric properties of the Dundee Ready Educational Environment Measure (DREEM) in a Moroccan sample of nursing students
Saka Khadija, Mohamed-Yassine Amarouch, Youssef Miyah, Mohammed Benjelloun, Jaouad El-Hilaly
Belitung Nursing Journal.2025; 11(2): 142. CrossRef
Revealing the significant shortcomings in the learning environment at the three largest medical schools in Syria: what’s next?
Ghaith Alfakhry, Ahmad Naeem, M. Bader AboHajar, Aisha Alfakhry, Abdul Fattah Mohandes, Iyad Ali, Ebrahim Makhoul, Nadeem Ahmed, M. Mhdy Abla, Khaled Alhomsi, Issam Jamous
BMC Medical Education.2023;[Epub] CrossRef
Medical Environment Assessment In Morocco From The Students’ Perspective: A National Survey

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Exploring student perceptions of the learning environment in four health professions education programs
Shayna A. Rusticus, Derek Wilson, Tal Jarus, Kathy O’Flynn-Magee, Simon Albon
Learning Environments Research.2022; 25(1): 59. CrossRef
Nurse students' perception of the academic learning environment in Tunisian institutes of nursing sciences: A multisite cross-sectional study
Khouloud Boukhris, Chekib Zedini, Mariem El Ghardallou
Nurse Education Today.2022; 111: 105316. CrossRef
Prévalence et facteurs associés à la détresse mentale chez les étudiants de la faculté de médecine de l’université de Parakou en 2020
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Ana Carolina Arantes Coutinho Costa, Nilce Maria da Silva Campos Costa, Edna Regina Silva Pereira
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Maha Lemtiri Chelieh, Murtaza Kadhum, Thomas Lewis, Andrew Molodynski, Redouane Abouqal, Jihane Belayachi, Dinesh Bhugra
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Journal of Educational Evaluation for Health Professions.2016; 13: 13. CrossRef

Research Articles

Profiling medical school learning environments in Malaysia: a validation study of the Johns Hopkins Learning Environment Scale: Sean Tackett, Hamidah Abu Bakar, Nicole A. Shilkofski, Niamh Coady, Krishna Rampal, Scott Wright; J Educ Eval Health Prof. 2015;12:39. Published online July 9, 2015; DOI: https://doi.org/10.3352/jeehp.2015.12.39

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188 Download
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Purpose
While a strong learning environment is critical to medical student education, the assessment of medical school learning environments has confounded researchers. Our goal was to assess the validity and utility of the Johns Hopkins Learning Environment Scale (JHLES) for preclinical students at three Malaysian medical schools with distinct educational and institutional models. Two schools were new international partnerships, and the third was school leaver program established without international partnership. Methods: First- and second-year students responded anonymously to surveys at the end of the academic year. The surveys included the JHLES, a 28-item survey using five-point Likert scale response options, the Dundee Ready Educational Environment Measure (DREEM), the most widely used method to assess learning environments internationally, a personal growth scale, and single-item global learning environment assessment variables. Results: The overall response rate was 369/429 (86%). After adjusting for the medical school year, gender, and ethnicity of the respondents, the JHLES detected differences across institutions in four out of seven domains (57%), with each school having a unique domain profile. The DREEM detected differences in one out of five categories (20%). The JHLES was more strongly correlated than the DREEM to two thirds of the single-item variables and the personal growth scale. The JHLES showed high internal reliability for the total score (α=0.92) and the seven domains (α= 0.56-0.85). Conclusion: The JHLES detected variation between learning environment domains across three educational settings, thereby creating unique learning environment profiles. Interpretation of these profiles may allow schools to understand how they are currently supporting trainees and identify areas needing attention.

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Quality of the educational environmentin Slovakia and the Czech Republic using the DREEM inventory
Ľubomíra Lizáková, Daša Stupková, Lucie Libešová, Ivana Lamková, Valéria Horanská, Ľudmila Andráščíková, Alena Lochmannová
Pielegniarstwo XXI wieku / Nursing in the 21st Century.2025; 24(1): 45. CrossRef
The impact of grade point average on medical students’ perception of the learning environment: a multicenter cross-sectional study across 12 Chinese medical schools
Yifan Liu, Donghao Lyu, Sujie Xie, Yuntao Yao, Jun Liu, Bingnan Lu, Wei Zhang, Shuyuan Xian, Jiale Yan, Meiqiong Gong, Xinru Wu, Yuanan Li, Haoyu Zhang, Jiajie Zhou, Yibin Zhou, Min Lin, Huabin Yin, Xiaonan Wang, Yue Wang, Wenfang Chen, Chongyou Zhang, Er
BMC Medical Education.2025;[Epub] CrossRef
A scoping review of the questionnaires used for the assessment of the perception of undergraduate students of the learning environment in healthcare professions education programs
Banan Mukhalalati, Ola Yakti, Sara Elshami
Advances in Health Sciences Education.2024; 29(4): 1501. CrossRef
Validation of the Polish version of the Johns Hopkins Learning Environment Scale–a confirmatory factor analysis
Dorota Wójcik, Leszek Szalewski, Adam Bęben, Iwona Ordyniec-Kwaśnica, Robert B. Shochet
Scientific Reports.2024;[Epub] CrossRef
Exploring medical students' experience of the learning environment: a mixed methods study in Saudi medical college
Mohammed Almansour, Noura Abouammoh, Reem Bin Idris, Omar Abdullatif Alsuliman, Renad Abdulrahman Alhomaidi, Mohammed Hamad Alhumud, Hani A. Alghamdi
BMC Medical Education.2024;[Epub] CrossRef
A multicenter cross-sectional study in China revealing the intrinsic relationship between medical students’ grade and their perceptions of the learning environment
Runzhi Huang, Weijin Qian, Sujie Xie, Mei Cheng, Meiqiong Gong, Shuyuan Xian, Minghao Jin, Mengyi Zhang, Jieling Tang, Bingnan Lu, Yiting Yang, Zhenglin Liu, Mingyu Qu, Haonan Ma, Xinru Wu, Huabin Yin, Xiaonan Wang, Xin Liu, Yue Wang, Wenfang Chen, Min Li
BMC Medical Education.2024;[Epub] CrossRef
Learning environment and its relationship with quality of life and burn-out among undergraduate medical students in Pakistan: a cross-sectional study
Saadia Shahzad, Gohar Wajid
BMJ Open.2024; 14(8): e080440. CrossRef
Validation of the Polish version of the DREEM questionnaire – a confirmatory factor analysis
Dorota Wójcik, Leszek Szalewski, Adam Bęben, Iwona Ordyniec-Kwaśnica, Sue Roff
BMC Medical Education.2023;[Epub] CrossRef
Association between patient care ownership and personal or environmental factors among medical trainees: a multicenter cross-sectional study
Hirohisa Fujikawa, Daisuke Son, Takuya Aoki, Masato Eto
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Measuring Students’ Perceptions of the Medical School Learning Environment: Translation, Transcultural Adaptation, and Validation of 2 Instruments to the Brazilian Portuguese Language
Rodolfo F Damiano, Aline O Furtado, Betina N da Silva, Oscarina da S Ezequiel, Alessandra LG Lucchetti, Lisabeth F DiLalla, Sean Tackett, Robert B Shochet, Giancarlo Lucchetti
Journal of Medical Education and Curricular Development.2020;[Epub] CrossRef
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Sarah Wallace Cater, Lakshmi Krishnan, Lars Grimm, Brian Garibaldi, Isabel Green
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Trainers' perception of the learning environment and student competency: A qualitative investigation of midwifery and anesthesia training programs in Ethiopia
Sharon Kibwana, Rachel Haws, Adrienne Kols, Firew Ayalew, Young-Mi Kim, Jos van Roosmalen, Jelle Stekelenburg
Nurse Education Today.2017; 55: 5. CrossRef

Learning styles and academic achievement among undergraduate medical students in Thailand: Wichuda Jiraporncharoen, Chaisiri Angkurawaranon, Manoch Chockjamsai, Athavudh Deesomchok, Juntima Euathrongchit; J Educ Eval Health Prof. 2015;12:38. Published online July 8, 2015; DOI: https://doi.org/10.3352/jeehp.2015.12.38

40,385 View
360 Download
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Abstract PDF

Purpose
This study aimed to explore the associations between learning styles and high academic achievement and to ascertain whether the factors associated with high academic achievement differed between preclinical and clinical students. Methods: A survey was conducted among undergraduate medical students in Chiang Mai University, Thailand. The Index of Learning Styles questionnaire was used to assess each student’s learning style across four domains. High academic achievement was defined as a grade point average of at least 3.0. Results: Of the 1,248 eligible medical students, 1,014 (81.3%) participated. Learning styles differed between the preclinical and clinical students in the active/reflective domain. A sequential learning style was associated with high academic achievement in both preclinical and clinical students. A reflective learning style was only associated with high academic achievement among preclinical students. Conclusion: The association between learning styles and academic achievement may have differed between preclinical and clinical students due to different learning content and teaching methods. Students should be encouraged to be flexible in their own learning styles in order to engage successfully with various and changing teaching methods across the curriculum. Instructors should be also encouraged to provide a variety of teaching materials and resources to suit different learning styles.

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