Gender trends in match rate to surgical specialties in Canada: A retrospective study from 2003–2022
Bibliographic record
Abstract
BACKGROUND: In Canada, there is a recognized underrepresentation of women in the field of surgery. However, the extent to which this trend applies across various surgical specialties is not well delineated. The aim of this study is to identify existing disparities and trends over time to inform the need for future interventions to make the match process more equitable for applicants. METHODS: Data regarding surgical specialty applicants was extracted from the Canadian Resident Matching Service (CaRMS)'s 2003 to 2022 reports. RESULTS: A total of 9,488 applicants ranked surgical specialties as their first choice from 2003-2022. Increases in the proportion of women applicants comparing periods 2003-2007 to 2018-2022 were significant for cardiac surgery (22% to 43%, p = 0.03), general surgery (46% to 60%, p<0.001), orthopedic surgery (23% to 35%, p<0.001), urology (23% to 38%, p<0.001), and all aggregated surgical specialties ('all surgery') (45% to 55%, p<0.001). An increase in the proportion of women applicants who matched over the same periods was observed for general surgery (+47% to 60%, p<0.001), orthopedic surgery (24% to 35%, p<0.01), urology (21% to 34%, p<0.001), and all surgery (46% to 54%, p<0.001). From 2003-2022, a lower match rate for women compared to men was observed for otolaryngology (0.60 v 0.69, p = 0.008), urology (0.61 v 0.72, p = 0.003), and all surgery (0.71 v 0.73, p = 0.038), while higher match rates were observed for ophthalmology (0.65 v 0.58, p = 0.04). No statistically significant differences in match rate were observed from 2018-2022. CONCLUSIONS: While the proportion of women applicants to surgical specialties in Canada has been increasing, women remain underrepresented in several surgical specialties. This underrepresentation cannot be solely attributed to fewer women applying to these specialties, as women experience lower success rates when matching to specific surgical specialties. Further research is essential to identify and address the underlying causes of these disparities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".