Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
School of Medicine, Trinity College Dublin, Dublin, Ireland.
Adv Health Sci Educ Theory Pract. 2024 Jul;29(3):829-840. doi: 10.1007/s10459-023-10283-2. Epub 2023 Sep 12.
A detailed, unbiased perspective of the inter-relations among medical fields could help students make informed decisions on their future career plans. Using a data-driven approach, the inter-relations among different medical fields were decomposed and clustered based on the similarity of their working environments.Publicly available, aggregate databases were merged into a single rich dataset containing demographic, working environment and remuneration information for physicians across Canada. These data were collected from the Canadian Institute for Health Information, the Canadian Medical Association, and the Institute for Clinical Evaluative Sciences, primarily from 2018 to 2019. The merged dataset includes 25 unique medical specialties, each with 36 indicator variables. Latent Profile Analysis (LPA) was used to group specialties into distinct clusters based on relatedness.The 25 medical specialties were decomposed into seven clusters (latent variables) that were chosen based on the Bayesian Information Criterion. The Kruskal-Wallis test identified eight indicator variables that significantly differed between the seven profiles. These variables included income, work settings and payment styles. Variables that did not significantly vary between profiles included demographics, professional satisfaction, and work-life balance satisfaction.The 25 analyzed medical specialties were grouped in an unsupervised manner into seven profiles via LPA. These profiles correspond to expected and meaningful groups of specialties that share a common theme and set of indicator variables (e.g. procedurally-focused, clinic-based practice). These profiles can help aspiring physicians narrow down and guide specialty choice.
详细、公正地了解医学领域之间的相互关系，可以帮助学生在未来的职业规划中做出明智的决策。本研究采用数据驱动的方法，根据工作环境的相似性，对不同医学领域之间的关系进行分解和聚类。将公开的聚合数据库合并到一个单一的丰富数据集，其中包含加拿大各地医生的人口统计学、工作环境和薪酬信息。这些数据来自加拿大卫生信息研究所、加拿大医学协会和临床评估研究所，主要收集于 2018 年至 2019 年。合并后的数据集包括 25 个独特的医学专业，每个专业有 36 个指标变量。潜在剖面分析（LPA）用于根据相关性将专业分组到不同的聚类中。25 种医学专业被分解为七个聚类（潜在变量），这些聚类是根据贝叶斯信息准则选择的。Kruskal-Wallis 检验确定了在七个剖面之间存在显著差异的八个指标变量。这些变量包括收入、工作环境和支付方式。在剖面之间没有显著差异的变量包括人口统计学、专业满意度和工作生活平衡满意度。通过 LPA，25 种分析的医学专业被以非监督的方式分组为七个剖面。这些剖面对应于具有共同主题和一组指标变量的预期和有意义的专业组（例如以程序为重点的诊所实践）。这些剖面可以帮助有抱负的医生缩小范围并指导专业选择。
