Fundamental Trends Within Falling Match Rates: Novel Insights from the Past Decade of Canadian Residency Matching Data

Abstract

The number of unmatched Canadian Medical Graduates (CMGs) has risen dramatically over the last decade. To identify long-term solutions to this problem, it is critical that we understand the factors contributing to these rising unmatched rates. Using match and electives data from 2009-2019, we employed machine learning algorithms to identify three clusters of disciplines with distinct trends in match and electives behaviours. We assessed the relationships between unmatched rates, competitiveness, rates of parallel planning, and program selection practices at a discipline level.

Across Canada, growth in CMGs has outpaced growth in residency seats, narrowing the seat-to-applicant ratio. Yet not all disciplines have been affected equally - a subset of surgical disciplines experienced a consistent decline in residency seats over time. Applicants to these disciplines are also at disproportionate risk of becoming unmatched, and this is associated with lower rates of parallel planning as quantified through clinical electives and match applications. This, in turn, is associated with the program selection practices of these disciplines.

Long term solutions to the unmatched CMG crisis require more nuance than indiscriminately increasing residency seats and should consider cluster specific match ratios as well as regulations around clinical electives and program selection practices.

Description

This repository accompanies our paper on the Canadian Residency Match. It contains cleaned Canadian Residency Matching (CaRMS) data from 2009 to 2019. It also contains analysis code to reproduce all of the main and supplementary figures from the manuscript.

Notebooks

There are 4 notebooks containing the analysis code for the paper. The HTML versions can be viewed with the following links:

Notebook 1 - Clustering of Disciplines:

1 - Discipline Clustering

Notebook 2 - Figures for Main Paper:

2 - Paper Figures

Notebook 3 - Supplemental Analysis (Feature Selection):

3 - Supplemental; Feature Selection

Notebook 4 - Supplemental Analysis (Cluster Heatmap & Feature Interactions):

4 - Supplemental; Figures S1 & S3