Gender differences in authorship of Canadian Urological Association guidelines
Bibliographic record
Abstract
INTRODUCTION: Women are under-represented in Canadian urology, particularly within academic leadership. This study aimed to analyze gender differences and trends in Canadian Urological Association (CUA) guideline authorship. METHODS: We searched the Canadian Urological Association Journal from March 2007 to August 2024 for all versions of eligible guidelines, best practice reports, and consensus statements. Two independent reviewers extracted data in duplicate. Authors appearing in multiple guidelines were counted more than once. We analyzed author characteristics by gender using the Chi-squared test and assessed authorship over time using the Cochran-Armitage test for trend. RESULTS: There were 1172 non-unique authors across 112 guidelines, of whom 750 (64%) were urologists. Women represented 15.5% of all authors and 7.5% of urologist authors. Focusing only on urologists, women were more likely to be first authors and to be included on functional, pediatric, and endourology guidelines than men. The proportion of women urologist authors, first authors, and last authors did not change significantly over time. Men and women urologists had similar rates of repeated authorship (56.7% vs. 51.7%, p=0.61), although men were more likely to appear on ≥5 guideline panels (23.6% vs. 6.9%, p=0.04). CONCLUSIONS: CUA guideline authorship is dominated by men, with limited progress in the participation of women over the past 18 years. CUA guideline panels help establish the standard of urologic care, and guideline authorship represents a significant academic opportunity. Further work to minimize this gender disparity is needed to ensure our guidelines better reflect the diversity of Canadian urologists, urology trainees, and patients.
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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 | 0.008 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".