Compensation benefits in a population-based cohort of men and women on long-term disability after musculoskeletal injuries: costs, course, predictors
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to assess costs, duration and predictors of prolonged compensation benefits by gender in a population characterised by long-term compensation benefits for traumatic or non-traumatic musculoskeletal injuries (MSIs). METHODS: This study examined 3 years of data from a register-based provincial cohort including all new allowed long-term claims (≥3 months of wage replacement benefits) related to neck/shoulder/back/trunk/upper-limb MSIs in Quebec, Canada, from 2001 to 2003 (13,073 men and 9032 women). Main outcomes were compensation duration and costs. Analyses were carried out separately for men and women to investigate gender differences. An extended Cox model with Heaviside functions of time was used to account for covariates with time-varying effects. RESULTS: Male workers experienced a longer compensation benefit duration and higher median costs. At the end of follow-up, 3 years postinjury, 12.3% of men and 7.3% of women were still receiving compensation benefits. Effects of certain predictors (e.g., income, injury site or industry) differed markedly between men and women. Age and claim history had time-varying effects in the men's and women's models, respectively. CONCLUSIONS: Knowing costs, duration and predictors of long-term compensation claims by gender can help employers, decision makers and rehabilitation specialists to identify at-risk workers and industries to engage them in early intervention and prevention programmes. Tailoring parts of long-term disability prevention and management efforts to men's and women's specific needs, barriers and vulnerable subgroups, could reduce time on benefits among both male and female long-term claimants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".