Promoting Organizational Change: A Urology Department-wide Wellness Program to Reduce Burnout
Bibliographic record
Abstract
INTRODUCTION: We developed a comprehensive wellness initiative to address burnout with specific interventions targeted at faculty, residents, nurses, administrators, coordinators, and other departmental personnel. METHODS: A department-wide wellness initiative was implemented in October 2020. General interventions included monthly holiday-themed lunches, weekly pizza lunches, employee recognition events, and initiation of a virtual networking board. Urology residents received financial education workshops, weekly lunches, peer support sessions, and exercise equipment. Faculty were offered personal wellness days to use at their discretion at no penalty to their calculated productivity. Administrative and clinical staff were given weekly lunches and professional development sessions. Pre- and post-intervention surveys included a validated single-item burnout instrument and the Stanford Professional Fulfillment Index. Outcomes were compared using Wilcoxon rank-sum tests and multivariable ordinal logistic regression. RESULTS: < .001). The highest-rated components were monthly gatherings (64%), sponsored lunches (58%), and employee of the month (53%). CONCLUSIONS: A department-wide wellness initiative with group-specific interventions can help reduce burnout and may improve professional fulfillment and workplace community.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".