Analysis of factors affecting Canadian medical students’ success in the residency match
Bibliographic record
Abstract
Background: In North America, there is limited data to support deliberate application strategies for post-graduate residency training. There is significant interest in determining what factors play a role in Canadian medical graduate (CMG) matching to their first choice discipline and heightened concern about the number of students going unmatched altogether. Methods: We analyzed matching outcomes of CMGs based on seven years (2013-2019) of residency application data (n= 13,499) from the Canadian Residency Matching Service (CaRMS) database using descriptive and binary logistic regression modeling techniques. Results: The sample was 54% female, with 60% between the ages of 26 and 29, and 60% attended medical schools in Ontario. Applicants who received more rankings from residency programs were more likely (OR = 1.185, p < 0.001) to match. Higher research activities (OR = 0.985, p < 0.001) and number of applications submitted (OR = 0.920, p < 0.001) were associated with a reduced likelihood of matching. Number of volunteer activities and self-report publications did not significantly affect matching. Being male (OR = 0.799, p < 0.05) aged <25 (OR = 0.756, p < 0.05), and from Eastern (OR = 0.497, p < 0.01), or Western (OR = 0.450, p < 0.001) Canadian medical schools were predictors of remaining unmatched. Conclusions: This study identified several significant associations of demographic and application factors that affected matching outcomes. The results will help to better inform medical student application strategies and highlight possible biases in the selection process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".