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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From GPT-3.5 to GPT-4.o: A Leap in AI’s Medical Exam Performance Markus Kipp Information.2024; 15(9): 543. CrossRef
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.
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.
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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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.
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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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.
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.
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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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).
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Medical Student’s Attitudes towards Implementation of National Licensing Exam (NLE) – A Qualitative Exploratory Study Saima Bashir, Rehan Ahmed Khan Pakistan Journal of Health Sciences.2024; : 153. CrossRef
Performance of ChatGPT on the Peruvian National Licensing Medical Examination: Cross-Sectional Study 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
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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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.
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.
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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
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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.
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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