Gender Equality in Plastic Surgery Training: A Canadian Nationwide Cross-sectional Analysis
Bibliographic record
Abstract
Introduction: One of the important factors in achieving gender equity is ensuring equitable surgical training for all. Previous studies have shown that females get significantly lower surgical exposure than males in certain surgical specialties. Gender gap in surgical exposure has never been assessed in plastic surgery. To that end, the goal of this study was to assess if there are any differences in plastic surgery training between male and female residents. Methods: A survey was sent to all plastic surgery residency programs in Canada to assess the No. of surgeries residents operated on as a co-surgeon or primary assistant during their training. The survey also assessed career goals, level of interest in the specialty, and subjective perception of gender bias. Results: A total of 89 plastic surgery residents (59.3% participation rate) completed the survey and were included in the study. The average No. of reconstructive cases residents operated on as a co-surgeon or primary assistant was 245 ± 312 cases. There was no difference in either reconstructive or aesthetic surgery case logs between male and female residents ( p > .05). However, a significantly larger proportion of females (39%) compared to males (4%) felt that their gender limited their exposure to surgical cases and led to a worsening of their overall surgical training ( p < .001). Finally, a larger proportion of male residents were interested in academic careers while a larger proportion of female residents were interested in a community practice ( p = .024). Conclusion: While there is no evidence of differences in the volume of logged cases between genders, female surgical residents still feel that their respective gender limits their overall surgical training. Gender inequalities in training should be addressed by residency programs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".