Work-Related Musculoskeletal Symptoms Among Canadian Gynecological Surgeons: A Cross-Sectional Survey Study
Bibliographic record
Abstract
Objective: To determine the frequency and burden of musculoskeletal (MSK) symptoms among gynecological surgeons practicing in Canada and to identify possible contributing factors. Methods: An online, cross-sectional survey was emailed to gynecological surgeons practicing across Canada. The survey included questions regarding surgeon demographics, MSK symptoms, the burden of these symptoms, and possible contributing factors. Results: Of the 254 survey respondents, 92.1% (234/254) (95% confidence interval [CI], 88–95%) reported work-related MSK symptoms. The most common symptoms experienced were pain, stiffness, muscle fatigue, and numbness/tingling. The most common body regions affected were the neck, lower back, and right shoulder. Overall, the burden of MSK symptoms was severe in 48% of respondents and mild or moderate in 44%. The odds of female surgeons experiencing a severe burden of MSK symptoms were significantly greater than for male surgeons (adjusted odds ratio [aOR] 2.62, 95% CI 1.26–5.43). Age <40 years (aOR 0.44, 95% CI 0.25–0.78), greater perceived fitness level (aOR 0.70; 95% CI 0.53–0.93), and a greater proportion of surgeries performed vaginally compared with laparoscopically (aOR 0.88, 95% CI 0.77–0.998) significantly reduced the odds of a severe burden of MSK symptoms. Conclusion: The findings underline the high frequency and severity of burden of MSK symptoms among Canadian gynecological surgeons, with a greater likelihood among female compared with male surgeons. Future research is warranted to identify strategies that could improve MSK health for gynecological surgeons.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".