Modified Work: Prevalence and Characteristics in a Sample of Workers With Soft-Tissue Injuries
Bibliographic record
Abstract
Modified-work programs are designed to facilitate the return to work for employees with a work-related injury. Although extensive published literature exists that describes and evaluates "ideal" programs, to date there is a paucity of data describing practice. To address this pertinent issue, we administered a survey to a large sample of 1833 workers with soft-tissue injuries in Ontario, Canada, and asked them detailed questions about modified work and employer contact. Our results reveal that most workers (66%) were contacted by someone from their workplace to check on how they were doing. However, only a minority (36%) were offered arrangements by their employer to help them return to work after developing a work-related soft-tissue injury. Most arrangements that were offered to injured workers consisted of such temporary modifications as reduced hours (24%), flexible work hours (25%), or a lighter job (57%) rather than more permanent changes to the way that work is conducted, such as changes to the work layout or equipment (8%). Merely being contacted by the workplace to check on how the worker was doing was not associated with reduced compensation benefit duration. Workplace offers of arrangements to help the worker return to work were associated with reduced compensation benefit duration but were not statistically associated with workers' pain grade.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".