Gender disparity within the Canadian Urological Association
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to examine gender diversity within the Canadian Urological Association (CUA) and to compare it with the Quebec Urological Association (QUA). METHODS: A retrospective review of women's representation regarding membership, committees' composition, awards, grants, and conferences between 2012 and 2022 was performed. Data and gender were extracted from databases and annual meeting programs provided by the CUA and the QUA. RESULTS: In 2022, females accounted for 18% (256/1431) of the membership at the CUA and 23% (52/228) at the QUA. The female proportion of committee members at the CUA increased from 9% (63/676) from 2012-2016 to 14% (177/1230) from 2017-2022 (p≤0.0001). In 2022, the QUA had a significantly higher proportion of female committee members than the CUA, with 39% (15/38) vs. 22% (50/225) women (p=0.0226), respectively. Moreover, from 2012-2022, 11% (5/46) of the CUA awards were given to women, whereas 38% (13/34) of the award winners at the QUA were women over the same time period (p=0.0038). Between 2012 and 2022, there were 16% (20/126) female CUA grant recipients and 44% (14/32) at the QUA (p=0.0095). The proportion of grants awarded to women at the CUA increased from 13% (5/39) in 2012-2016 to 17% (15/87) in 2017-2022. Two percent (1/53) of the plenary invited speakers at the CUA annual meetings from 2012-2016 were women, compared with 21% (14/66) from 2017-2022 (p=0.0016). In 2022, 53% (9/17) of invited plenary faculty were women at the QUA annual conference, compared to 23% (3/13) at the CUA annual meeting (p=0.0980). CONCLUSIONS: Over the past 10 years, there has been an increase in women's representation at the CUA and the QUA; however, data show that the increase in female representation at the QUA has outpaced that of the CUA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 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) | 0.007 | 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".