Differential underestimation of work‐related reinjury risk for older workers: Challenges to producing accurate rate estimates
Bibliographic record
Abstract
BACKGROUND: Older workers are increasingly represented in the U.S. workforce, but frequently work part-timeor intermittently, hindering accurate injury rate estimates. To reduce the impact of reporting barriers on rate comparisons, we focused on reinjury (both injury recurrence and new injury) among workers with a workers' compensation claim, assessing: (1) reinjury risk for workers age 65+ versus <65; (2) importance of work-time at-risk measurement for rate estimates and comparisons; and (3) age distribution of potential risk factors. METHODS: Washington State workers' compensation claims for a retrospective cohort of workers with work-related permanent impairments were linked to state wage files. Reinjury rates were calculated for the cohort (N = 11,184) and a survey sample (N = 582), using both calendar time and full-time equivalent (FTE)-adjusted time. Risk differentials were assessed using rate ratios and adjusted survival models. RESULTS: The rate ratio for workers age 65+ (vs. <65) was 0.45 (p < 0.001) using calendar time, but 0.70 (p = 0.07) using FTE-adjusted time. Survey-based rates were 35.7 per 100 worker-years for workers age 65+, versus 14.8 for <65. Workers age 65+ (vs. <65) were more likely to work <100% FTE, but were similar regarding job strain, their ability to handle physical job demands, and their comfort reporting unsafe conditions or injuries. CONCLUSIONS: Accounting for work-time at risk substantially improves age-based reinjury comparisons. Although the marked elevation in self-reported reinjury risk for older workers might be a small-sample artifact (n = 34), workers age 65+ are likely at higher risk than previously appreciated. Ongoing workforce trends demand increased attention to injury surveillance and prevention for older workers.
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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 | 0.318 | 0.604 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".