Workplace improvements to support safe and sustained return to work: Suggestions from a survey of workers with permanent impairments
Bibliographic record
Abstract
BACKGROUND: Roughly 10% of occupational injuries result in permanent impairment. After initial return to work (RTW), many workers with permanent impairments face RTW interruption due to reinjury, unstable health, disability, and layoff. This study used open-ended survey data to: (1) explore workplace factors identified by workers as important levers for change, some of which may previously have been unrecognized; and (2) summarize workers' suggestions for workplace improvements to promote sustained RTW and prevent reinjury. METHODS: This study included data from workers' compensation claims and telephone surveys of 582 Washington State workers who had RTW after a work-related injury involving permanent impairment. The survey was conducted in 2019, about a year after claim closure. We used qualitative content analysis methods to inductively code open-ended survey responses. RESULTS: The most frequent themes were: safety precautions/safer workplace (18.1%), adequate staffing/appropriate task distribution (16.2%), and safety climate (14.1%). Other frequent themes included ergonomics, rest breaks, job strain, predictability and flexibility in work scheduling practices, employer response to injury, social support, communication, and respect. Many workers reported that they were not listened to, or that their input was not sought or valued. Workers often linked communication deficiencies to preventable deficiencies in safety practices, safety climate, and RTW practices, and also to lack of respect or distrust. In counterpoint, nearly one-third of respondents reported that no change was needed to their workplace. CONCLUSIONS: Policies and interventions targeting worker-suggested workplace improvements may promote safe and sustained RTW, which is essential for worker health and economic stability.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".