Abstract
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.
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10459_2023_10283_MOESM1_ESM.tiff
Supplementary Material 1: Supplementary Fig. 1: BIC values for different mixture model parameterizations showing 1 to 19 components. The red dotted line corresponds to the EII model with maximum BIC in this graph. The blue dotted line corresponds to the EEI model with a similar BIC yet larger, more meaningful groups of specialties. Thus, the EEI model with seven components was selected to derive medical profiles from medical specialties.
10459_2023_10283_MOESM2_ESM.tiff
Supplementary Material 2: Supplementary Fig. 2: Parallel coordinates plot illustrating the distinction between profiles for different (aggregate) indicators. Asterisks indicate significant differences with Benjamini-Hochberg correction. Abbreviations: FFS = Fee-for-service; FTE = Full-time equivalent; Org.=Organization; WS = Work Setting
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Balas, M., Scheepers, R.M., Zador, Z. et al. Profiling medical specialties and informing aspiring physicians: a data-driven approach. Adv in Health Sci Educ (2023). https://doi.org/10.1007/s10459-023-10283-2
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DOI: https://doi.org/10.1007/s10459-023-10283-2