Initial return to work and long‐term employment patterns: Associations with work‐related permanent impairment and with participation in workers' compensation‐based return‐to‐work programs
Bibliographic record
Abstract
BACKGROUND: Roughly 10% of injured workers experience work injuries that result in permanent impairment and a permanent partial disability (PPD) award. This study aimed to characterize and quantify long-term employment outcomes for injured workers, by the degree of whole body impairment (WBI) and by participation in several workers' compensation (WC)-based return-to-work (RTW) programs. METHODS: A retrospective cohort of 43,968 Washington State workers were followed for up to 10 years after WC claim closure (2009-2017). Degree of impairment was classified as: (1) no PPD award, (2) PPD award with WBI < 10%, or (3) PPD award with WBI ≥ 10%. State wage files were used to construct employment outcomes for regression, modeling: (1) time to first RTW, (2) time to first RTW interruption, (3) RTW volatility, and (4) employment gaps. RESULTS: Wage patterns and employment outcomes differed significantly by the degree of impairment. Compared to other workers, workers with WBI ≥ 10% had delayed RTW, shorter average times to first RTW interruption, and higher rates of both RTW interruptions and quarters without wages. Time to first RTW averaged over a year, increasing with the degree of impairment. About 9% overall-and 27% of workers with ≥10% WBI-had no observed wages after claim closure. In adjusted models, workers with WBI ≥ 10% had significantly poorer employment outcomes, compared to workers with no PPD award (p < 0.001). CONCLUSIONS: State wage files provide an efficient approach to identifying RTW patterns. Workers with permanent impairment were at substantially higher risk of poor employment outcomes. WC-based RTW programs may promote better employment outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".