Ageing workers with work-related musculoskeletal injuries
Bibliographic record
Abstract
BACKGROUND: Older workers often take longer to recover and experience more missed workdays after work-related injuries, but it is unclear why or how best to intervene. Knowing the characteristics of older injured workers may help in developing interventions to reduce the likelihood of work disability. AIMS: To describe and compare several characteristics between younger and middle-aged working adults (25-54 years), adults nearing retirement (55-64 years) and adults past typical retirement (≥65 years), who sustained work-related musculoskeletal injuries. METHODS: In this cross-sectional study, Alberta workers' compensation claimants with subacute and chronic work-related musculoskeletal injuries were studied. A wide range of demographic, employment, injury and clinical characteristics were investigated. Descriptive statistics were computed and compared between the age groups. RESULTS: Among 8003 claimants, adults 65 years or older, compared to those 25-54 and 55-64 years, had lower education (16 versus 10 and 12%, P < 0.001) and were more likely to work in trades, transport and related occupations (50 versus 46 and 44%, P < 0.001), to have less offers of modified work (57 versus 39 and 42%, P < 0.001), more fractures (18 versus 14 and 11%, P < 0.001) and no further rehabilitation recommended after assessment (28 versus 18 and 20%, P < 0.01). CONCLUSIONS: Injured workers past typical retirement age appeared to be a disadvantaged group with significant challenges from a vocational rehabilitation perspective. They were less likely to have modified work options available or be offered rehabilitation, despite having more severe injuries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".