Gender trends in orthopedic surgical residency programs in Canada over 20 years
Bibliographic record
Abstract
Background: Gender disparities exist in several surgical specialties, particularly in orthopedic surgery. The purpose of this study was to determine the current trends in gender diversity among orthopedic surgical residents in Canada over the last 20 years. Methods: We analyzed gender distribution data for orthopedic trainees from the Canadian Resident Matching Service (CaRMS) for 2013–2022 and the Canadian Post-MD Education Registry for 2000–2022 using linear and quadratic regressions. Results: More male (4.7%) than female medical students (1.9%) applied to an orthopedics program (p < 0.001). The proportion of male applicants entering CaRMS who applied to orthopedics followed a quadratic (U-shaped) distribution over time (p = 0.01). The proportion of female applicants remained unchanged from 2013 to 2022 (p > 0.9). However, for matching results, among the applicants who applied to orthopedic surgery, there was no gender effect (men 56.1% matched, women 50.3% matched; p = 0.3). The proportion of residency spots offered to female applicants remained at around 30%, without significant changes over time (p = 0.1). The number of female orthopedic graduates increased linearly from 2000 to 2021 (p < 0.001), projected to reach gender equalization (at 50%) by 2060. Female residents experienced higher attrition in residency (3.4%) than male residents (2.1%; p = 0.05), and this gender difference is decreasing over time (p = 0.03). Conclusion: Over 2 decades, women have shown consistently lower rates of application to orthopedic surgery programs than their male colleagues. Women who matched experienced higher attrition rates than men, although this appears to be improving over time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".