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Exploration of examinees' traits that affect the score of Korean Medical Licensing Examination.

ABSTRACT: PURPOSE: It aims to identify the effect of five variables to score of the Korean Medical Licensing Examinations (KMLE) for three consecutive years from 2011 to 2013. METHODS: The number of examinees for each examination was 3,364 in 2011 3,177 in 2012, and 3,287 in 2013. Five characteristics of examinees were set as variables: gender, age, graduation status, written test result (pass or fail), and city of medical school. A regression model was established, with the score of a written test as a dependent variable and with examinees' traits as variables. RESULTS: The regression coefficients in all variables, except the city of medical school, were statistically significant. The variable's effect in three examinations appeared in the following order: result of written test, graduation status, age, gender, and city of medical school. CONCLUSION: written test scores of the KMLE revealed that female students, younger examinees, and first-time examinees had higher performances.

SUBMITTER: Yim MK

PROVIDER: S-EPMC4397840 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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Exploration of examinees' traits that affect the score of Korean Medical Licensing Examination.

Yim Mi Kyoung MK

Journal of educational evaluation for health professions 20150316

<h4>Purpose</h4>It aims to identify the effect of five variables to score of the Korean Medical Licensing Examinations (KMLE) for three consecutive years from 2011 to 2013.<h4>Methods</h4>The number of examinees for each examination was 3,364 in 2011 3,177 in 2012, and 3,287 in 2013. Five characteristics of examinees were set as variables: gender, age, graduation status, written test result (pass or fail), and city of medical school. A regression model was established, with the score of a writte ...[more]

PMID: 25773516

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Project description:Traditional Korean medicine (TKM) emphasizes individualized diagnosis and treatment. This uniqueness makes AI modeling difficult due to limited data and implicit processes. Large language models (LLMs) have demonstrated impressive medical inference, even without advanced training in medical texts. This study assessed the capabilities of GPT-4 in TKM, using the Korean National Licensing Examination for Korean Medicine Doctors (K-NLEKMD) as a benchmark. The K-NLEKMD, administered by a national organization, encompasses 12 major subjects in TKM. GPT-4 answered 340 questions from the 2022 K-NLEKMD. We optimized prompts with Chinese-term annotation, English translation for questions and instruction, exam-optimized instruction, and self-consistency. GPT-4 with optimized prompts achieved 66.18% accuracy, surpassing both the examination's average pass mark of 60% and the 40% minimum for each subject. The gradual introduction of language-related prompts and prompting techniques enhanced the accuracy from 51.82% to its maximum accuracy. GPT-4 showed low accuracy in subjects including public health & medicine-related law, internal medicine (2), and acupuncture medicine which are highly localized in Korea and TKM. The model's accuracy was lower for questions requiring TKM-specialized knowledge than those that did not. It exhibited higher accuracy in diagnosis-based and recall-based questions than in intervention-based questions. A significant positive correlation was observed between the consistency and accuracy of GPT-4's responses. This study unveils both the potential and challenges of applying LLMs to TKM. These findings underline the potential of LLMs like GPT-4 in culturally adapted medicine, especially TKM, for tasks such as clinical assistance, medical education, and research. But they also point towards the necessity for the development of methods to mitigate cultural bias inherent in large language models and validate their efficacy in real-world clinical settings.

Project description:PurposeThis study aimed to gather opinions from medical educators on the possibility of introducing an interview to the Korean Medical Licensing Examination (KMLE) to assess professional attributes. Specifically following topics were dealt with: the appropriate timing and tool to assess unprofessional conduct; the possiblity of prevention of unprofessional conduct by introducing an interview to the KMLE; and the possibility of implementation of an interview to the KMLE.MethodsA cross-sectional study approach based on a survey questionnaire was adopted. We analyzed 104 pieces of news about doctors’ unprofessional conduct to determine the deficient professional attributes. We derived 24 items of unprofessional conduct and developed the questionnaire and surveyed 250 members of the Korean Society of Medical Education 2 times. Descriptive statistics, cross-tabulation analysis, and Fisher’s exact test were applied to the responses. The answers to the open-ended questions were analyzed using conventional content analysis.ResultsIn the survey, 49 members (19.6%) responded. Out of 49, 24 (49.5%) responded in the 2nd survey. To assess unprofessional conduct, there was no dominant timing among basic medical education (BME), KMLE, and continuing professional development (CPD). There was no overwhelming assessment tool among written examination, objective structured clinical examination, practice observation, and interview. Response rates of “impossible” (49.0%) and “possible” (42.9%) suggested an interview of the KMLE prevented unprofessional conduct. In terms of implementation, “impossible” (50.0%) was selected more often than “possible” (33.3%).ConclusionProfessional attributes should be assessed by various tools over the period from BME to CPD. Hence, it may be impossible to introduce an interview to assess professional attributes to the KMLE, and a system is needed such as self-regulation by the professional body rather than licensing examination.

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Exploration of examinees' traits that affect the score of Korean Medical Licensing Examination.

Publications

Exploration of examinees' traits that affect the score of Korean Medical Licensing Examination.

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