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Identifying and supporting students at risk of failing the National Medical Licensure Examination in Japan using a predictive pass rate

BACKGROUND: Students who fail to pass the National Medical Licensure Examination (NMLE) pose a huge problem from the educational standpoint of healthcare professionals. In the present study, we developed a formula of predictive pass rate (PPR)” which reliably predicts medical students who will fail...

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Autores principales: Tsunekawa, Koji, Suzuki, Yasuyuki, Shioiri, Toshiki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654142/
https://www.ncbi.nlm.nih.gov/pubmed/33167945
http://dx.doi.org/10.1186/s12909-020-02350-8
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author Tsunekawa, Koji
Suzuki, Yasuyuki
Shioiri, Toshiki
author_facet Tsunekawa, Koji
Suzuki, Yasuyuki
Shioiri, Toshiki
author_sort Tsunekawa, Koji
collection PubMed
description BACKGROUND: Students who fail to pass the National Medical Licensure Examination (NMLE) pose a huge problem from the educational standpoint of healthcare professionals. In the present study, we developed a formula of predictive pass rate (PPR)” which reliably predicts medical students who will fail the NMLE in Japan, and provides an adequate academic support for them. METHODS: Six consecutive cohorts of 531 medical students between 2012 and 2017, Gifu University Graduate School of Medicine, were investigated. Using 7 variables before the admission to medical school and 10 variables after admission, we developed a prediction formula to obtain the PPR for the NMLE using logistic regression analysis. In a new cohort of 106 medical students in 2018, we applied the formula for PPR to them to confirm the capability of the PPR and predicted students who will have a strong likelihood of failing the NMLE. RESULTS: Medical students who passed the NMLE had the following characteristics: younger age at admission, graduates of high schools located in the surrounding area, high scores in the graduation examination and in the comprehensive computer-based test provided by the Common Achievement Test Organization in Japan. However, total score of examination in pre-clinical medical sciences and Pre-CC OSCE score in the 4th year were not correlated with the PPR. Ninety-one out of 531 students had a strong likelihood of failing the NMLE between 2012 and 2017 and 33 of these 91 students failed NMLE. Using the PPR, we predicted 12 out of 106 students will have a strong likelihood of failing the NMLE. Actually, five of these 12 students failed NMLE. CONCLUSIONS: The PPR can be used to predict medical students who have a higher probability of failing the NMLE. This prediction would enable focused support and guidance by faculty members. Prospective and longitudinal studies for larger and different cohorts would be necessary. SUPPLEMENTARY INFORMATION: Supplementary information accompanies this paper at 10.1186/s12909-020-02350-8.
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spelling pubmed-76541422020-11-10 Identifying and supporting students at risk of failing the National Medical Licensure Examination in Japan using a predictive pass rate Tsunekawa, Koji Suzuki, Yasuyuki Shioiri, Toshiki BMC Med Educ Research Article BACKGROUND: Students who fail to pass the National Medical Licensure Examination (NMLE) pose a huge problem from the educational standpoint of healthcare professionals. In the present study, we developed a formula of predictive pass rate (PPR)” which reliably predicts medical students who will fail the NMLE in Japan, and provides an adequate academic support for them. METHODS: Six consecutive cohorts of 531 medical students between 2012 and 2017, Gifu University Graduate School of Medicine, were investigated. Using 7 variables before the admission to medical school and 10 variables after admission, we developed a prediction formula to obtain the PPR for the NMLE using logistic regression analysis. In a new cohort of 106 medical students in 2018, we applied the formula for PPR to them to confirm the capability of the PPR and predicted students who will have a strong likelihood of failing the NMLE. RESULTS: Medical students who passed the NMLE had the following characteristics: younger age at admission, graduates of high schools located in the surrounding area, high scores in the graduation examination and in the comprehensive computer-based test provided by the Common Achievement Test Organization in Japan. However, total score of examination in pre-clinical medical sciences and Pre-CC OSCE score in the 4th year were not correlated with the PPR. Ninety-one out of 531 students had a strong likelihood of failing the NMLE between 2012 and 2017 and 33 of these 91 students failed NMLE. Using the PPR, we predicted 12 out of 106 students will have a strong likelihood of failing the NMLE. Actually, five of these 12 students failed NMLE. CONCLUSIONS: The PPR can be used to predict medical students who have a higher probability of failing the NMLE. This prediction would enable focused support and guidance by faculty members. Prospective and longitudinal studies for larger and different cohorts would be necessary. SUPPLEMENTARY INFORMATION: Supplementary information accompanies this paper at 10.1186/s12909-020-02350-8. BioMed Central 2020-11-10 /pmc/articles/PMC7654142/ /pubmed/33167945 http://dx.doi.org/10.1186/s12909-020-02350-8 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Tsunekawa, Koji
Suzuki, Yasuyuki
Shioiri, Toshiki
Identifying and supporting students at risk of failing the National Medical Licensure Examination in Japan using a predictive pass rate
title Identifying and supporting students at risk of failing the National Medical Licensure Examination in Japan using a predictive pass rate
title_full Identifying and supporting students at risk of failing the National Medical Licensure Examination in Japan using a predictive pass rate
title_fullStr Identifying and supporting students at risk of failing the National Medical Licensure Examination in Japan using a predictive pass rate
title_full_unstemmed Identifying and supporting students at risk of failing the National Medical Licensure Examination in Japan using a predictive pass rate
title_short Identifying and supporting students at risk of failing the National Medical Licensure Examination in Japan using a predictive pass rate
title_sort identifying and supporting students at risk of failing the national medical licensure examination in japan using a predictive pass rate
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654142/
https://www.ncbi.nlm.nih.gov/pubmed/33167945
http://dx.doi.org/10.1186/s12909-020-02350-8
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