Age, occupational demands and the risk of serious work injury
Bibliographic record
Abstract
BACKGROUND: Interest in the relationship between age and serious work injury is increasing, given the ageing of the workforce in many industrialized economies. AIMS: To examine if the relationship between age and risk of serious musculoskeletal injury differs when the physical demands of work are higher from those when they are lower. METHODS: A secondary analysis of workers' compensation claims in the State of Victoria, Australia, combined with estimates of the insured labour force. We focused on musculoskeletal claims, which required 10 days of absence or health care expenditures beyond a pecuniary threshold. Regression models examined the relationship between age and claim-risk across workers with different occupational demands, as well as the relationship between occupational demands and musculoskeletal claim-risk across different age groups. RESULTS: Older age and greater physical demands at work were associated with an increased risk of musculoskeletal claims. In models stratified by occupational demands, we observed the relationship between age and claim-risk was steeper when occupational demands were higher. We also observed that the relationship between occupational demands and risk of work injury claim peaked among workers aged 25-44, attenuating among those aged 45 and older. CONCLUSIONS: This study's results suggest that although older workers and occupations with higher demands should be the targets of primary preventive efforts related to serious musculoskeletal injuries, there may also be gains in targeting middle-aged workers in the most physically demanding occupations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".