背景:我国医学生流失率较高。本研究利用列线图技术,基于19个个体和工作相关特征,建立了中国医学本科生辍学意愿的预测模型。
方法:进行了重复的横断面研究,通过滚雪球抽样,在T1(2020年8月至2021年4月)和T2(2022年10月)的969名参与者中招收3536名医学本科生。人口统计(年龄,性别,研究阶段,收入,关系状态,精神病史)和心理健康因素(包括抑郁症,焦虑,压力,倦怠,酒精使用障碍,困倦,生活质量,疲劳,自杀企图史(SA),和躯体症状),以及与工作相关的变量(职业选择遗憾和原因,工作场所暴力经历,以及对中国医疗保健环境的总体满意度),是通过问卷收集的。来自T1的数据分为训练队列和内部验证队列,而T2数据作为外部验证队列。对列线图的性能进行了区分评估,校准,临床适用性,并使用接收器工作特性曲线(ROC)进行泛化,曲线下面积(AUC),校正曲线,和决策曲线分析(DCA)。
结果:从19个个人和工作相关因素来看,五个被确定为构建列线图的重要预测因子:SA的历史,职业选择遗憾,工作场所暴力的经验,抑郁症状,和倦怠。训练的AUC值,内部验证,和外部验证队列分别为0.762,0.761和0.817.列线图证明了可靠的预测和区分,在训练和验证队列中进行充分的校准和概括。
结论:此列线图在预测中国医学本科生的辍学意向方面具有合理的准确性。它可以指导大学,医院,和政策制定者确定学生处于危险之中,从而告知有针对性的干预措施。解决潜在因素,如抑郁症状,倦怠,职业选择遗憾,工作场所暴力可能有助于减少医学本科生的流失。
背景:这是一项观察性研究。没有与此手稿相关的临床试验编号。
BACKGROUND: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
BACKGROUND: This is an observational study. There is no Clinical Trial Number associated with this manuscript.