Effects of sex and age on work-related upper extremity musculoskeletal disorders in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Effective targeting of workplace upper extremity musculoskeletal disorder (MSD) prevention strategies requires the identification of demographic groups most at risk. Workers' compensation data provides an effective means of surveillance of MSDs at the population level. OBJECTIVE: The primary purpose of this study was to identify the effects of age and sex on rates of tendon injuries of the wrist and hand, carpal tunnel syndrome (CTS), epicondylitis, and soft tissue shoulder injuries in Ontario, Canada between 2000-2019 using workers' compensation data from the Association of Workers Compensation Boards of Canada (AWCBC). METHODS: Age and sex specific rates of lost-time injury claims from the four identified injury categories as well as "non-specific" upper extremity MSDs which did not fit into the four categories were calculated by standardizing injury claim totals with "at-risk" population estimates from the Canada Labour Force Survey. A multiple regression analysis was used to analyze the effects of age and sex on rates of specific injury claims. RESULTS: Statistically significant age and sex effects were identified for rates of claims from tendon injuries of the wrist and hand, CTS, and shoulder injuries, while only age effects were significant for epicondylitis. Between 2000-2019, rates of claims from the four injury categories studied and the magnitude of the age and sex effects declined substantially over time. CONCLUSION: Detailed surveillance of workplace ergonomic hazards in Ontario workplaces is needed to determine what is causing rates of upper extremity claims to decline.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".