Gender-Based Disparity in Academic Ranking and Research Productivity Among Canadian Anesthesiology Faculty
Bibliographic record
Abstract
Purpose Despite increasing numbers of women entering anesthesiology training, women remain underrepresented in senior academic positions and leadership roles. This study aims to determine the extent of gender disparity in Canadian departments of anesthesiology. In addition, we explore the correlation between publication productivity and academic rank in this cohort. Methods The Canadian Residency Matching Service (CaRMS) was queried to identify 17 training programs for anesthesiology. Department websites were searched to determine the names of faculty members, as well as gender, leadership roles, and academic ranks. The SCOPUS© database was used to generate the number of publications, number of citations, publication range, and h-index of each faculty member. Results In our study cohort of 1404 academic anesthesiologists, 30.1% were women. Women held a minority of 130 leadership positions (27%, n = 35). With increasing academic rank female representation decreased (p = 0.009), such that 21% of full professors were women. Overall, male anesthesiologists had a higher h-index, number of publications, and number of citations (p = 0.001, p = 0.001, and p = <0.001, respectively) than women. Conclusion Despite growing numbers of women entering the academic workforce, women are underrepresented in senior academic ranks and leadership positions. In addition, men and women have significant differences in measures of publication productivity. This study underscores the importance of directed efforts to promote equity in career outcomes.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".