Mental ill-health and second claims for work-related injury
Bibliographic record
Abstract
BACKGROUND: There is some evidence that mental ill-health (MIH) is associated with injury at work, but data are sparse. AIMS: To examine, within a cohort of workers with a first workers' compensation claim, whether those with a history of MIH had a higher than expected number of second claims. METHODS: All Workers' Compensation Board (WCB) records from January 1995 to December 2004 were linked to administrative health records, and a physician diagnosis of MIH in the 48 months prior to the first WCB claim extracted. The first and second (if any) claim for each worker were identified and time to second claim calculated. Survival time to second claim was estimated by Cox regression with history of MIH as a covariate. RESULTS: Results were available for 389 903 WCB first claimants. Of these 53% of men and 38% of women had a second claim, with a mean time between claims of 768 days (men) and 785 days (women). Those with a history of MIH were somewhat more likely to make a second claim and, in the survival analysis, to make this claim sooner. Type of injury at first claim did not appear to modify this effect. CONCLUSIONS: Workers with a recent history of MIH at the time of making a first WCB claim for a work injury are at greater risk of a second injury, leading to a new claim. Strategies to get workers back to work after the first injury/claim should include management of MIH to reduce the risk of further injury.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".