Factors affecting work-related shoulder pain
Bibliographic record
Abstract
BACKGROUND: Work-related shoulder pain is a common problem. Ergonomic factors in the workplace are thought to be important but a number of other factors have also been associated with shoulder pain. AIMS: To identify risk factors for work-related shoulder pain in Alberta, focusing particularly on ergonomic risk factors. METHODS: A case referent design was used to compare individuals who made a Workers' Compensation Board (WCB) claim for work-related shoulder pain with individuals who made a claim for other types of injury. Data were collected using a postal questionnaire and analysed by logistic regression. RESULTS: There were 1263 participants (562 cases, 701 referents). The participation rate was 25% among cases and 21% among referents (P < 0.01). Factors associated with an increased likelihood of claim for shoulder injury included lifting ≥10 kg above shoulder height for ≥15 min per day, shoulder pain in the month prior to injury, working in the 'Government, education, and health services' industry sector and being occasionally/never satisfied with support from colleagues. CONCLUSIONS: These results support the importance of ergonomic factors in work-related shoulder pain claims, particularly the lifting of weights above shoulder level for even short periods. Relatively simple ergonomic measures, such as restricting above shoulder lifting, could be adopted with the aim of reducing the risk of shoulder injury at work.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".