Applicant gender and matching to first-choice discipline: a cross-sectional analysis of data from the Canadian Resident Matching Service (2013–2019)
Bibliographic record
Abstract
BACKGROUND: Previous studies examining potential sex and gender bias in the Canadian Resident Matching Service (CaRMS) match have had conflicting results. We examined the results of the CaRMS match over the period 2013-2019 to determine the potential association between applicants' gender and the outcome of matching to their first-choice discipline. METHODS: In this cross-sectional analysis, we determined the risk of matching to one's first-choice discipline in CaRMS by applicant gender and year, for all Canadian medical students who participated in the first iteration of the R-1 match for the years 2013 to 2019. We analyzed data in 3 categories of disciplines according to CaRMS classifications: family medicine, nonsurgical disciplines and surgical disciplines. We excluded disciplines with fewer than 10 applicants. RESULTS: Match results were available for 20 033 participants, of whom 11 078 (55.3%) were female. Overall, female applicants were significantly more likely to match to their first-choice discipline (relative risk [RR] 1.03, 95% confidence interval [CI] 1.02-1.04). After adjustment for match year and stratification by discipline categories, we found that female applicants were more likely to match to family medicine as their first choice (RR 1.04, 95% CI 1.03-1.05) and less likely to match to a first-choice surgical discipline (RR 0.95, 95% CI 0.91-1.00) than their male peers. There was no significant difference between the genders in matching to one's first-choice nonsurgical discipline (RR 1.01, 95% CI 0.99-1.03). INTERPRETATION: These results suggest an association between an applicant's gender and the probability of matching to one's first-choice discipline. The possibility of gender bias in the application process for residency programs should be further evaluated and monitored.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".