Gender representation in professorship and research productivity across all surgical specialties in Canadian academic institutions
Bibliographic record
Abstract
Background Disparate gender representation among Canadian academic surgeons is documented; however, the association of academic rank with research productivity across all surgical specialties is not well understood. Our objective was to assess differences in gender representation by academic rank and research productivity metrics for surgical specialties in Canadian academic centres. Methods This retrospective, cross-sectional, comparative study used online public databases in 2021. Data sources included the Canadian Resident Matching Service program descriptions, College of Physicians and Surgeons databases, the Scopus platform, and professional websites. Gender distribution by academic rank, research productivity metrics, institution, and surgical specialty were tested for a 0.5 proportion rate. We used a generalized logistic regression model adjusting for confounders to assess gender association with ordinally ranked academic rank. We defined significance by p < 0.05 with reported 95% confidence intervals. Results We assessed 10 surgical specialties across 17 Canadian academic institutions. Women surgeons were underrepresented in 16 out of 17 centres ( p < 0.001), comprising the majority in only obstetrics–gynecology ( p < 0.001). Women were also less represented as assistant (37%), associate (27%), and full professors (18%) ( p < 0.001), with lower mean h -index (6.4, p < 0.001), years active in research (11.5, p < 0.001), number of publications (18, p < 0.001), and m -quotient (0.42, p < 0.001). Multivariate analysis showed that men were more likely to be represented in senior professorship regardless of research productivity, institution, and specialty determinants (odds ratio 1.30–1.33, p = 0.001–0.024). Conclusion Women surgeons were underrepresented across all academic ranks, were less likely to achieve senior professorship, and had lower research productivity metrics.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".