Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Tsunekawa K

PROVIDER: S-EPMC7654142 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

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

Tsunekawa Koji K Suzuki Yasuyuki Y Shioiri Toshiki T

BMC medical education 20201110 1

<h4>Background</h4>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.<h4>Methods</h4>Six consecutive cohorts of 531 medical students between 2012 and 2017, Gifu University Graduate School of M ...[more]

PMID: 33167945

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Project description:BackgroundImperial College School of Medicine, London UK, introduced a new curriculum in 2019, with a focus on the GMC outcomes for graduates, and pedagogy best practice. The new curriculum included formative assessments, named engagement and feedback assessments (EFAs), to support learning, and attainment in the summative examinations. The aims of this study were to assess the validity of EFAs and to determine whether they have utility as a modified form of programmatic assessment to inform decision-making regarding possible interventions by measuring and analysing attendance at and performance in these formative events.MethodsSeven hundred and sixty-one students were included in the study and assessment results were included for academic years 2019/20 to 2020/21. Forty-one data points per student, (27 in Year 1 and 14 in Year 2) were used, to compare EFA scores with the summative performance. Attendance was monitored through engagement with the EFAs.ResultsCohort 1 (enrolled 2019): In year 1, EFAs were associated with summative exam scores (overall r = 0.63, p < 0.001). Year 2, EFA scores were also associated with summative scores (overall r = 0.57, p < 0.001), including the clinical practical assessment (r = 0.45, p < 0.001). Missing two or more EFAs was associated with a significant increase in the likelihood of failing one or more summative examinations in the first year (OR: 7.97, 95% CI 2.65-34.39) and second year (OR: 3.20, 95% CI 1.74-5.95). Missing more than two EFAs in their first year was also associated with a higher risk of failing a summative examination in the second year (OR: 2.47, 95% CI 1.33-4.71). Students who increased their attendance between year 1 and 2 fared better in summative assessment than those who maintained poor attendance, whereas those that reduced their attendance fared worse than those that maintained high attendance. Cohort 2 (enrolled 2020): Analysis of cohort 2 supported these findings and in this cohort missing two or more EFAs was again associated with an increased likelihood of failing a summative examination (OR = 4.00, 95% CI = 2.02-7.90).ConclusionOur EFA model has validity in predicting performance in summative assessments and can inform prospective interventions to support students' learning. Enhancing attendance and engagement can improve outcomes.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

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