Work–Life Integration, Professional Stress, and Gender Disparities in the Urological Workforce: Findings from a Worldwide Cross-Sectional Study
Bibliographic record
Abstract
Background/Objectives: Physician burnout and mental health issues are widespread, with over 50% experiencing burnout and nearly 25% suffering from depression, trends that have worsened since 2018. High-demand specialties like urology face additional stressors, including increasing workloads and technological changes. Gender disparities further exacerbate these challenges, with female urologists reporting higher burnout and work–life balance struggles. To evaluate perceptions of work–life balance, career satisfaction, and workplace experiences among urologists worldwide, and to provide potential strategies to improve physician well-being, promote gender equity, and support the sustainability of urology. Methods: A web-based, cross-sectional survey was conducted from March to June 2025, involving urologists, residents, and fellows globally. The 30-item questionnaire covered demographics, working conditions, work–life balance, and gender-related workplace issues. Data were analyzed using descriptive statistics stratified by gender, age, role, and region. Results: We received replies from 390 doctors in urology. Work-related stress was reported by 87.4% (340). A total of 17.7% (69) felt their career progression to be fully compatible with their personal life, while 42.3% (165) perceived a significant imbalance. Female urologists experienced higher perceptions of inequality in career and work–life opportunities. Over 50% expressed willingness to reduce workload for family reasons, highlighting systemic barriers. Burnout was most prevalent among younger urologists (<50 years), with persistent gender disparities across regions. Conclusions: Work–life imbalance and burnout remain major concerns for urologists globally, especially among female and early-career physicians. Addressing these issues requires institutional policies promoting flexibility, gender equity, and targeted support. Further research is needed to develop effective interventions to sustain a resilient urological workforce.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".