Journal of Educational Evaluation for Health Professions

Editorials

Halted medical education and medical residents’ training in Korea, journal metrics, and appreciation to reviewers and volunteers: Sun Huh; J Educ Eval Health Prof. 2025;22:1. Published online January 13, 2025; DOI: https://doi.org/10.3352/jeehp.2025.22.1

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Journal of Educational Evaluation for Health Professions received the top-ranking Journal Impact Factor―9.3—in the category of Education, Scientific Disciplines in the 2023 Journal Citation Ranking by Clarivate: Sun Huh; J Educ Eval Health Prof. 2024;21:16. Published online June 24, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.16

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The emergence of generative artificial intelligence platforms in 2023, journal metrics, appreciation to reviewers and volunteers, and obituary: Sun Huh; J Educ Eval Health Prof. 2024;21:9. Published online April 11, 2024; DOI: https://doi.org/10.3352/jeehp.2024.21.9

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Halted medical education and medical residents’ training in Korea, journal metrics, and appreciation to reviewers and volunteers
Sun Huh
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Editorial policies of Journal of Educational Evaluation for Health Professions on the use of generative artificial intelligence in article writing and peer review: Sun Huh; J Educ Eval Health Prof. 2023;20:40. Published online December 31, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.40

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Journal of Educational Evaluation for Health Professions received the Journal Impact Factor, 4.4 for the first time on June 28, 2023: Sun Huh; J Educ Eval Health Prof. 2023;20:21. Published online June 29, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.21

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Issues in the 3rd year of the COVID-19 pandemic, including computer-based testing, study design, ChatGPT, journal metrics, and appreciation to reviewers: Sun Huh; J Educ Eval Health Prof. 2023;20:5. Published online January 31, 2023; DOI: https://doi.org/10.3352/jeehp.2023.20.5

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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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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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Editorials

What happened during the period from senior medical students’ withdrawal of their applications to take the Korean Medical Licensing Examination in August 2020 to their taking the licensing examination in February 2021: Sun Huh; J Educ Eval Health Prof. 2022;19:3. Published online January 28, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.3

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Application of computer-based testing in the Korean Medical Licensing Examination, the emergence of the metaverse in medical education, journal metrics and statistics, and appreciation to reviewers and volunteers: Sun Huh; J Educ Eval Health Prof. 2022;19:2. Published online January 13, 2022; DOI: https://doi.org/10.3352/jeehp.2022.19.2

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Journal statistics, coping strategy with upcoming scholarly journal publishing environment including Plan-S, and appreciation for reviewers and volunteers: Sun Huh; J Educ Eval Health Prof. 2019;16:41. Published online December 28, 2019; DOI: https://doi.org/10.3352/jeehp.2019.16.41

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Journal of Obesity & Metabolic Syndrome: A Platform for Acquiring and Disseminating the Most Recent Research Findings and Developing Research Concepts about Obesity
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Research article

Goodness of fit of the items used in the 2nd cycle of evaluation and accreditation of medical schools by the Korea Institute of Medical Education and Evaluation based on the Rasch model: Man Sup Lim, Sun Huh; J Educ Eval Health Prof. 2019;16:28. Published online September 30, 2019; DOI: https://doi.org/10.3352/jeehp.2019.16.28

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Purpose
Since 2004, the Korea Institute of Medical Education and Evaluation has been responsible for the evaluation and accreditation of medical schools in Korea. The 2nd cycle of evaluations was conducted from 2007 to 2011. The present study aimed at testing the goodness of fit of the items used in the 2nd cycle of evaluation and accreditation based on the Rasch model.
Methods
Dichotomous data on 40 medical schools were analyzed using Winsteps, a tool based on the Rasch model that includes goodness-of-fit testing.
Results
Two of the 109 items had an outfit mean square exceeding 2.0. The other 107 items showed a goodness of fit in the acceptable range for the outfit mean square. All items were in the acceptable range in terms of the infit mean square. Furthermore, 1 school had an outfit mean square exceeding 2.0, while all schools were in the acceptable range for the infit mean square. An outfit mean square value over 2.0 means that an item is a outlier. Therefore, 2 items showed an extreme response relative to the overall response. Meanwhile, the finding of an outfit mean square over 2.0 for 1 school means that it showed extraordinary responses to specific items, despite its excellent overall competency.
Conclusion
The goodness of fit of the items used for evaluation and accreditation by the Korea Institute of Medical Education and Evaluation should be checked so that they can be revised appropriately. Furthermore, the outlier school should be investigated to determine why it showed such an inappropriate goodness of fit.

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