Health status, work limitations, and return-to-work trajectories in injured workers with musculoskeletal disorders
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to describe the health status and work limitations in injured workers with musculoskeletal disorders at 1 month post-injury, stratified by return-to-work status, and to document their return-to-work trajectories 6 months post-injury. METHODS: A sample of 632 workers with a back or upper extremity musculoskeletal disorder, who filed a Workplace Safety and Insurance Board lost-time claim injury, participated in this prospective study. Participants were assessed at baseline (1 month post-injury) and at 6 months follow-up. RESULTS: One month post-injury, poor physical health, high levels of depressive symptoms and high work limitations are prevalent in workers, including in those with a sustained first return to work. Workers with a sustained first return to work report a better health status and fewer work limitations than those who experienced a recurrence of work absence or who never returned to work. Six months post-injury, the rate of recurrence of work absence in the trajectories of injured workers who have made at least one return to work attempt is high (38%), including the rate for workers with an initial sustained first return to work (27%). CONCLUSIONS: There are return-to-work status specific health outcomes in injured workers. A sustained first return to work is not equivalent to a complete recovery from musculoskeletal disorders.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".