Thesen Thomas, Marrero Wesley J, Konopasky Abigail J, Duncan Matthew S, Blackmon Karen E
Department of Medical Education, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA.
Department of Computer Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA.
Med Teach. 2025 Apr;47(4):630-634. doi: 10.1080/0142159X.2024.2357279. Epub 2024 May 29.
Medical trainee well-being is often met with generalized solutions that overlook substantial individual variations in mental health predisposition and stress reactivity. Precision medicine leverages individual environmental, genetic, and lifestyle factors to tailor preventive and therapeutic interventions. In addition, an exclusive focus on clinical mental illness tends to disregard the importance of supporting the positive aspects of medical trainee well-being. We introduce a novel precision well-being framework for medical education that is built on a comprehensive and individualized view of mental health, combining measures from mental health and positive psychology in a unified, data-driven framework. Unsupervised machine learning techniques commonly used in precision medicine were applied to uncover patterns within multidimensional mental health data of medical students. Using data from 3,632 US medical students, clusters were formulated based on recognized metrics for depression, anxiety, and flourishing. The analysis identified three distinct clusters. Membership in the 'Healthy Flourishers' well-being phenotype was associated with no signs of anxiety or depression while simultaneously reporting high levels of flourishing. Students in the 'Getting By' cluster reported mild anxiety and depression and diminished flourishing. Membership in the 'At-Risk' cluster was associated with high anxiety and depression, languishing, and increased suicidality. Nearly half (49%) of the medical students surveyed were classified as 'Healthy Flourishers', whereas 36% were grouped into the 'Getting-By' cluster and 15% were identified as 'At-Risk'. Findings show that a substantial portion of medical students report diminished well-being during their studies, with a significant number struggling with mental health challenges. This novel precision well-being framework represents an integrated empirical model that classifies individual medical students into distinct and meaningful well-being phenotypes based on their holistic mental health. This approach has direct applicability to student support and can be used to evaluate the effectiveness of personalized intervention strategies stratified by cluster membership.
医学实习生的幸福感常常采用一些通用的解决方案，而这些方案忽视了心理健康易感性和应激反应中存在的巨大个体差异。精准医学利用个体的环境、基因和生活方式因素来定制预防和治疗干预措施。此外，单纯关注临床精神疾病往往会忽视支持医学实习生幸福感积极方面的重要性。我们引入了一种新颖的医学教育精准幸福感框架，该框架基于对心理健康的全面和个性化观点构建，将心理健康和积极心理学的措施整合在一个统一的数据驱动框架中。运用精准医学中常用的无监督机器学习技术来揭示医学生多维心理健康数据中的模式。利用来自3632名美国医学生的数据，根据公认的抑郁、焦虑和蓬勃发展指标形成了不同的类别。分析确定了三个不同的类别。属于“健康蓬勃发展者”幸福感表型的学生没有焦虑或抑郁的迹象，同时报告称有较高的蓬勃发展水平。“勉强维持”类别的学生报告有轻度焦虑和抑郁，且蓬勃发展水平较低。属于“高危”类别的学生与高焦虑、抑郁、萎靡不振以及自杀倾向增加有关。近一半（49%）接受调查的医学生被归类为“健康蓬勃发展者”，而36%被归为“勉强维持”类别，15%被确定为“高危”。研究结果表明，很大一部分医学生在学习期间报告幸福感下降，相当数量的学生在心理健康方面面临挑战。这种新颖的精准幸福感框架代表了一种综合实证模型，它根据医学生的整体心理健康状况将其分类为不同且有意义的幸福感表型。这种方法对学生支持具有直接适用性，可用于评估按类别成员分层的个性化干预策略的有效性。
