Department of Psychiatry and Psychotherapy, Gifu University Graduate School of Medicine, Gifu, 467-8601, Japan.
Department of Medical Career and Professional Development, Nagoya City University School of Medical Sciences, 1, Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, 467-8601, Japan.
BMC Med Educ. 2024 Aug 27;24(1):930. doi: 10.1186/s12909-024-05948-4.
Failure of students to pass the National Medical Licensure Examination (NMLE) is a major problem for universities and the health system in Japan. To assist students at risk for NMLE failure as early as possible after admission, this study investigated the time points (from the time of admission to graduation) at which predictive pass rate (PPR) can be used to identify students at risk of failing the NMLE.
Seven consecutive cohorts of medical students between 2012 and 2018 (n = 637) at the Gifu University Graduate School of Medicine were investigated. Using 7 variables before admission to medical school and 10 variables after admission, a prediction model to obtain the PPR for the NMLE was developed using logistic regression analysis at five time points, i.e., at admission and the end of the 1st, 2nd, 4th, and 6th grades. All students were divided into high (PPR < 95%) and low (PPR ≥ 95%) risk groups for failing the NMLE at the five time points, respectively, and the movement between the groups during 6 years in school was simulated.
Medical students who passed the NMLE had statistically significant factors at each of the 5 time points, and the number of significant variables increased as their grade in school advanced. In addition, two factors extracted at admission were also selected as significant variables at all other time points. Especially, age at entry had a consistent and significant effect during medical school.
Risk analysis based on multiple variables, such as PPR, can inform more effective intervention compared to a single variable, such as performance in the mock exam. A longer prospective study is required to confirm the validity of PPR.
学生未能通过国家医师执照考试（NMLE）是日本大学和医疗系统的主要问题。为了尽早帮助入学后可能无法通过 NMLE 的学生，本研究调查了可以使用预测通过率（PPR）来识别有 NMLE 失败风险的学生的时间点（从入学到毕业）。
本研究调查了 2012 年至 2018 年连续七届医学生（n=637）。使用入学前的 7 个变量和入学后的 10 个变量，通过逻辑回归分析在五个时间点（入学时和第 1、2、4 和 6 年级结束时）建立获得 NMLE PPR 的预测模型。所有学生分别分为五个时间点 NMLE 失败的高（PPR<95%）和低（PPR≥95%）风险组，并模拟在校 6 年内两组之间的变化。
通过 NMLE 的医学生在五个时间点的每个时间点都有统计学意义的因素，随着年级的升高，显著变量的数量也在增加。此外，入学时提取的两个因素也被选为所有其他时间点的显著变量。特别是，入学时的年龄在校期间一直具有一致且显著的影响。
与单一变量（如模拟考试成绩）相比，基于 PPR 等多个变量的风险分析可以提供更有效的干预措施。需要进行更长时间的前瞻性研究来确认 PPR 的有效性。
