Urologist burnout: Frequency, causes, and potential solutions to an unspoken entity
Bibliographic record
Abstract
Physician burnout has been linked to decreased job performance, increased medical errors, interpersonal conflicts, and depression. Recent multispecialty studies suggest that urologists have higher rates (up to 63.6%) of burnout compared to physicians in other specialties; however, these reports were limited by low sample sizes.1 We aimed to evaluate the prevalence of urologist burnout, verify risk factors, and recommend preventative measures and solutions for colleagues at risk or suffering from burnout. Urologist burnout is a true entity that transcends level of training and nationality. Its roots appear to be deep-seated in our tireless efforts to strive for excellence in care for our patients, our growing academic and research pursuits, and surmounting administrative responsibilities; these virtues, which are regarded as the foundations of our career successes, are often obtained at the expense of personal health and wellbeing, as well as family sacrifice. Various other medical societies have become increasingly vocal about the issue of physician burnout and have actively initiated successful strategies to minimize its impact on their members. As an organization with a strong national presence, the Canadian Urological Association (CUA) should promote tools to prevent and interventions to assist those at risk for and suffering from burnout. Increased awareness in the general medical community has led to strategies and tools that can help prevent, identify, or assist physicians in their recovery from burnout. The CUA should develop and facilitate access to information and offer comprehensive support for urologists struggling with burnout.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".