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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".