Academic Gender Disparity in Orthopedic Surgery in Canadian Universities
Bibliographic record
Abstract
Introduction Academic medicine is notorious for being "male-dominated." We hypothesized that there were significant and quantifiable levels of gender disparity in academic orthopedic surgery, and this article attempts to quantify the extent of the existing disparity. Also, we examined the research productivity of academic faculty in orthopedic surgery and its correlation with academic ranks and leadership positions. Methods Our study design was cross-sectional in nature. We searched the Canadian Resident Matching Service (CaRMS) to compile a list of medical schools that offer orthopedic surgery training for residency. A total of 713 academic orthopedic surgeons met our inclusion criteria. Of the 713 orthopedic surgeons, 518 had an H-index score available on Elsevier's Scopus (Elsevier, Amsterdam, Netherlands). The gender, academic rank, leadership position, and H-index were compared. Data analysis was done with Statistical Package for the Social Sciences (SPSS; IBM, Armonk, NY). The binomial negative regression was used to compare the average H-index between men and women at each rank. Results Our study results reveal that academic orthopedic surgery in Canada is male-dominated, with men holding 87% of the academic positions. Female academic orthopedic surgeons held lower academic ranks, such as assistant professor or lecturer. Women orthopedic surgeons had lower H-index scores compared to their counterparts in ranks above the assistant professor. Our findings imply that research productivity and the ratio of average H-index scores comparing men to women (HM/HF) grow larger with each academic rank. At a 90% confidence level, women were less likely to hold leadership positions than men at an odds ratio (OR) of 0.52 [90% confidence interval (CI): 0.29-0.925, p: 0.03]. There were no significant differences in H-index between men and women for departmental leadership positions. Conclusion Women were underrepresented in number, rank, and academic productivity (H-index). We offer possible factors that may have contributed to this finding as well as potential solutions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".