Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China.
BMC Med Educ. 2024 Aug 12;24(1):868. doi: 10.1186/s12909-024-05835-y.
The attrition rate of Chinese medical students is high. This study utilizes a nomogram technique to develop a predictive model for dropout intention among Chinese medical undergraduates based on 19 individual and work-related characteristics.
A repeated cross-sectional study was conducted, enrolling 3536 medical undergraduates in T1 (August 2020-April 2021) and 969 participants in T2 (October 2022) through snowball sampling. Demographics (age, sex, study phase, income, relationship status, history of mental illness) and mental health factors (including depression, anxiety, stress, burnout, alcohol use disorder, sleepiness, quality of life, fatigue, history of suicidal attempts (SA), and somatic symptoms), as well as work-related variables (career choice regret and reasons, workplace violence experience, and overall satisfaction with the Chinese healthcare environment), were gathered via questionnaires. Data from T1 was split into a training cohort and an internal validation cohort, while T2 data served as an external validation cohort. The nomogram's performance was evaluated for discrimination, calibration, clinical applicability, and generalization using receiver operating characteristic curves (ROC), area under the curve (AUC), calibration curves, and decision curve analysis (DCA).
From 19 individual and work-related factors, five were identified as significant predictors for the construction of the nomogram: history of SA, career choice regret, experience of workplace violence, depressive symptoms, and burnout. The AUC values for the training, internal validation, and external validation cohorts were 0.762, 0.761, and 0.817, respectively. The nomogram demonstrated reliable prediction and discrimination, with adequate calibration and generalization across both the training and validation cohorts.
This nomogram exhibits reasonable accuracy in foreseeing dropout intentions among Chinese medical undergraduates. It could guide colleges, hospitals, and policymakers in pinpointing students at risk, thus informing targeted interventions. Addressing underlying factors such as depressive symptoms, burnout, career choice regret, and workplace violence may help reduce the attrition of medical undergraduates.
This is an observational study. There is no Clinical Trial Number associated with this manuscript.
中国医学生的流失率较高。本研究利用列线图技术，基于 19 项个体和与工作相关的特征，为中国医学生制定了辍学意向预测模型。
通过滚雪球抽样，在 T1（2020 年 8 月至 2021 年 4 月）阶段招募了 3536 名医学生，在 T2（2022 年 10 月）阶段招募了 969 名参与者。通过问卷调查收集人口统计学数据（年龄、性别、学习阶段、收入、关系状况、精神病史）和心理健康因素（包括抑郁、焦虑、压力、倦怠、酒精使用障碍、困倦、生活质量、疲劳、自杀未遂史和躯体症状），以及与工作相关的变量（职业选择后悔和原因、工作场所暴力经历、以及对中国医疗保健环境的整体满意度）。T1 阶段的数据分为训练队列和内部验证队列，T2 阶段的数据作为外部验证队列。通过接收者操作特征曲线（ROC）、曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估列线图的判别能力、校准、临床适用性和推广性。
从 19 个个体和与工作相关的因素中，确定了五个对列线图构建有显著预测作用的因素：自杀未遂史、职业选择后悔、工作场所暴力经历、抑郁症状和倦怠。训练、内部验证和外部验证队列的 AUC 值分别为 0.762、0.761 和 0.817。该列线图具有可靠的预测和判别能力，在训练和验证队列中均具有良好的校准和推广性。
该列线图在预测中国医学生辍学意向方面具有合理的准确性。它可以为学院、医院和政策制定者提供识别有风险学生的方法，从而指导有针对性的干预措施。解决抑郁症状、倦怠、职业选择后悔和工作场所暴力等潜在因素可能有助于减少医学生的流失。
