Sex Disparity Among Canadian Cardiologists in Academic Medicine: Differences in Scholarly Productivity and Academic Rank
Bibliographic record
Abstract
Background Women remain relatively underrepresented in all subspecialties of academic medicine. While sex disparity is prevalent in a number of specialties, the association between academic productivity and sex in academic cardiology has not been assessed in the Canadian context. Methods Academic faculty of accredited Canadian Resident Matching Service (CaRMS) programs were included from cardiology division websites across 17 universities. Cardiology faculty members' names, academic ranks, leadership positions, and sex were obtained from each institutions' website. The Elsevier database Scopus© was used to extract the Hirsch index (H-index), years of active research, and number of publications of each faculty member. The H-index was used as a metric of academic output and research productivity. Univariate regression was run with the H-Index as the outcome of interest, and multiple linear regression analysis was used to determine factors associated with higher H-index. Results Sex was identified for 1,040 members, of whom 836 (80%) were male. Male members had higher numbers of publications (p <0.001). There was a trend for males in a leadership position to have a higher H-index (p = 0.07). Median H-index was lower for women (p = 0.02). Males across assistant and associate professor ranks had a higher H-index. Women achieving professor rank demonstrated greater productivity with a higher median H-index (p = 0.002). Conclusions There is a prevalent sex gap in academic cardiology with regard to scholarly productivity and academic achievement. Factors that may help narrow the sex gap need to be identified and corrective measures implemented to enhance sex equity.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 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) | 0.006 | 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".