Survey of Occupational Musculoskeletal Pain and Injury in Canadian Optometry
Bibliographic record
Abstract
Objective: A growing concern in optometry is the incidence and prevalence of occupational musculoskeletal (MSK) pain and injury, with no studies assessing Canadian professionals. However, the risk of work-related MSK pain in optometry has become widely recognized by the profession and literature as having a negative impact on the health and careers of optometrists. The goal of this study was to quantify prevalence and location of MSK pain in Canadian optometrists and estimate practices that may be associated with MSK issues. Methods: A voluntary, internet-based survey (SurveyMonkey Inc., San Mateo, CA, United States) was distributed to all optometrists registered with the Canadian Association of Optometry (CAO). Survey questions were adapted from the literature to identify the prevalence and significance of work-related MSK issues. Results: One-hundred-twenty-one optometrists, with a response rate of 2.4%, and 169 ophthalmologists, with a response rate of 17% participated in a voluntary internet-based survey. When asked if they had experienced pain attributed to their work in the last 12 months, 61% of optometry responders said “yes”, compared to 50% of ophthalmology responders (p=0.06). Shoulder pain was reported in 41% of optometry responders, lower back pain in 37% and neck pain in 34%. This was compared to 28% (p=0.02), 36% (p=0.90) and 46% (p=0.04) respectively in ophthalmology responders. Optometry respondents most commonly attributed MSK pain to “performing the same task over and over”, “working in the same position” and “slit lamp exams”. Conclusion: Many of the eye-care responders in our study were impacted by work-related MSK pain. The prevalence, location and severity of pain were similar to findings in other literature. More research is needed to determine best practices for prevention and solution of MSK pain among eye-care professionals.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".