Gender Differences in Research Productivity among Academic Psychiatrists in Canada
Bibliographic record
Abstract
Objectives: Gender inequity in academic medicine persists despite increases in the number of women physicians. We sought to explore gender differences in research productivity for academic psychiatrists in Canada. Methods: In a cross-sectional study of the 3379 psychiatrists in all 17 university departments of psychiatry in Canada, research productivity, as measured by the h-index and number of publications, was compared between women and men using a negative log binomial regression model to generate relative rates (RRs), adjusted for career duration (aRR). Findings were stratified by academic rank, institution region, and institution size. A subanalysis of those with 10 or more publications was conducted as a proxy for identifying physicians on a research track. Results: Women (43% of the sample) had a lower mean (standard deviation) h-index than men (2.87 [6.49] vs. 5.31 [11.1]; aRR, 0.62; 95% confidence interval [CI], 0.54 to 0.72). Differences were significant only for junior faculty and not for associate and full professors. Comparison by number of publications followed a similar pattern (aRR, 0.46; 95% CI, 0.39 to 0.55). Among those with 10 or more publications ( n = 721), differences between men and women were smaller than in the overall cohort for both the h-index (aRR, 0.77; 95% CI, 0.68 to 0.87) and number of publications (aRR, 0.62; 95% CI, 0.53 to 0.72). Conclusions: Gender differences in research productivity at the national level in academic psychiatry in Canada support a call to adopt a more systematic approach to promoting equitable opportunities for women in research, especially in early career, to improve diversity and enhance future psychiatric research and discovery.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".