Exploring the patterns of practice and satisfaction among female urologists in Canada
Bibliographic record
Abstract
INTRODUCTION: Our aim was to explore the satisfaction, personal and professional challenges, and practice barriers among female urologists in Canada. METHODS: A literature review was completed to design our survey. Trends with respect to career and personal satisfaction were identified, including academic advancement, mentorship, professional challenges, workplace discrimination, family satisfaction, and remuneration, among others. These key themes were formatted into 44 questions, translated into French, and distributed electronically as a survey to 80 female urology staff across Canada. RESULTS: Sixty (75.0%) women completed the survey. Many had been in practice <5 years (44.1%) and 72.9% completed a fellowship. Overall, 96.6% of women were very or somewhat satisfied with their career. Seeing more time-consuming patients and financial constraints within the healthcare system were the greatest source of dissatisfaction. Two-thirds of respondents reported that they received significant mentorship and 40% found it difficult to find a mentor during their training. Overall, 65.0% experienced gender discrimination, most commonly from a colleague or a patient. Women who practiced in the community were more likely to report experiencing discrimination compared to women practicing in an academic setting (78.1% vs. 51.9%; p=0.034). Mean time for maternity leave was 17.1 (±8.3) weeks, and 30.2% reported a pregnancy-related complication triggered by their work. Overall, 66.1% would choose urology again. CONCLUSIONS: It is important to advocate for the wellness of female urologists. To accomplish this, we need to address the challenges revealed in the survey, including supporting women on maternity leave, improving mentorship, and prioritizing female urology leadership initiatives. We have established a formal circle of support within the urology community in Canada to achieve these goals.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| 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) | 0.002 | 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".