National Trends of Gender Disparity in Canadian Cardiovascular Society Guideline Authors, 2001-2020
Bibliographic record
Abstract
Background The level of representation of women in cardiology remains low compared to that of men, particularly in leadership positions. We evaluated gender disparity in the authorship of Canadian Cardiovascular Society (CCS) guidelines. Methods All CCS guidelines from 2001-2020 were identified. Gender was assessed based on pronoun use in the biographies and social media of the authors. Only primary panel authors were included in our analysis. Stratified analyses were performed based on subspecialties. Results A total of 76 guidelines were identified, with 1172 authors (26% women, 74% men, P < 0.0001), with no significant change in percentage of women authors over 2 decades , (37.1% in 2001, 36.3% in 2020, P = 0.34). Inclusion of women as authors occurred less frequently than inclusion of men in general cardiology guidelines (20.1% vs 79.9%, P < 0.0001) and all subspecialties—heart failure (36.4% vs 63.6%, P < 0.0001), interventional cardiology (12.6% vs 87.4%, P < 0.0001), electrophysiology (20.2% vs 79.8%, P < 0.0001), and pediatric cardiology (41.7% vs 58.3%, P = 0.02). It was less likely for women to be a chair or cochair of a guideline writing committee, compared with men (20.1% vs 79.8%, P < 0.0001). There were 609 unique authors (25.6% women, 74.4% men, P < 0.0001), 542 unique medical doctorate (MD) authors (20.7% women, 79.3% men, P < 0.0001), and 67 unique non-MD authors (65.7% women, 34.3% men, P = 0.0003). Conclusions There is a persistent shortfall in the inclusion of women authors for CCS guidelines, which has not changed over time. Further efforts are required to promote women's inclusion in leadership roles, which may lead to authorship of the guidelines.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".