Gender Diversity in Canadian Surgical Residency
Bibliographic record
Abstract
Background: Diversity of gender representation in surgery is known to positively influence patient outcomes and predict career trajectories for female trainees. This study aims to identify the current and recent past state of gender diversity amongst trainees entering Canadian surgical residency programs. Methods: Data were sourced from the Canadian Post-M.D. Education Registry (CAPER) and the Canadian Resident Matching Service (CaRMs) for ten surgical specialties. CAPER data include PGY-1 trainees in all surgical specialties for the academic years 2012–2013 to 2021–2022. CaRMs provided data of total applicants and matched applicants for Canadian Medical Graduates (CMGs) in the match years 2013–2022. Results: From 2012–2022, there were 4011 PGY-1 surgical residents across Canada (50.4% female, 49.6% male). The surgical specialties with the most female representation were obstetrics/gynecology (82.1–91.9%), general surgery (40.2–70.7%), and plastic surgery (33.3–55.6%). The surgical specialties with the least female representation were neurosurgery (18.7–35.3%), urology (11.8–42%), and orthopedic surgery (17.5–38.5%). The number of female applicants to surgical programs has increased since 2013 and outnumbers male applicants each subsequent year. The match rate to surgical programs for female applicants has varied by year, with the highest being 63.9% in 2014 and the lowest in 2018 at 48.8%. Conclusions: Our study shows promising trends that reflect increased representation of female trainees. However, while the number of female trainees in general surgery and obstetrics/gynecology programs matches and even exceeds Canadian demographic proportions, this is not true for most other surgical specialties. This calls for continued efforts to improve and retain gender equity across surgical specialties in Canada.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".