Gender disparity within academic Canadian urology
Bibliographic record
Abstract
INTRODUCTION: Increasing female matriculation into medical school has shown an increase in women training in academic urology, but gender disparity still exists within this male-dominated field. This study aims to evaluate publication productivity and rank differences of Canadian female and male academic urologists. METHODS: was consulted to tabulate the number of documents published, citations, and h-index of each faculty member. To account for temporal bias associated with the h-index, the m-quotient was also computed. RESULTS: There was a significantly higher number of men (164, 88.17%) among academic faculty than women (22, 11.83%). As academic rank increased, the proportion of female urologists decreased. Overall, male urologists had higher academic ranks, h-index values, number of publications, and citations (p=0.038, p=0.0038, p=0.0011, and p=0.014, respectively). There was an insignificant difference between men and women with respect to their m-quotient medians (p=0.25). CONCLUSIONS: There is an increasing number of women completing residency in urology, although there are disproportionally fewer female urologists at senior academic positions. Significant differences were found in the h-index, publication count, and citation number between male and female urologists. When using the m-quotient to adjust for temporal bias, no significant differences were found between the gender in terms of academic output.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.003 | 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) | 0.012 | 0.001 |
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".