Prevalence of Work-Related Pain or Discomfort Among Urologists in the State of Florida: Results From the Florida Urologic Society Task Force on Ergonomic Challenges Experienced by Its Members
Bibliographic record
Abstract
Abstract Background Pain and work-related musculoskeletal disorders are commonly seen in surgeons, significantly impacting quality of life and burnout. A questionnaire-based study was conducted to further investigate the nature and etiology of work-related pain among urologists in the state of Florida. Objective This study aimed to quantify the number of urologists who reported work-related musculoskeletal disorders >25% of the time. Methods The Florida Urologic Society Task Force developed a survey based on the Nordic Musculoskeletal Questionnaire, with additional input from Cornell’s ergonomic studies. The Mayo Clinic Survey Research Center conducted the survey and distributed it to 504 members of the Florida Urologic Society in 2023. Results The total response rate was 18.6% (94/504). The primary outcome (number of urologists who reported pain >25% of the time) was 45.3% (34/75). In total, 32.4% (22/68) of the respondents reported pain associated with endoscopic surgery >25% of the time, 40.0% (14/35) reported pain for major open cases, 20.6% (13/63) reported pain for minor open cases, and 22.7% (5/22) reported pain for robotic cases. In total, 68.8% (53/77) of the respondents attributed their work-related pain to uncomfortable operating positions, and 29.9% (23/77) chose to ignore their pain. Conclusions In this contemporaneous population of Florida urologic surgeons, almost half of the respondents describe having work-related pain >25% of the time. The data show that major open surgery had the highest rate of pain, followed closely by endoscopic surgery. Over 70% of the urologists in Florida are interested in official ergonomics training, which, if developed, may lead to increased productivity and better emotional, personal, and interpersonal well-being.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".