Examining age differences in duration of wage replacement by injury characteristics
Bibliographic record
Abstract
BACKGROUND: One explanation for why older age is associated with greater duration of wage replacement following a work-related injury may be that older workers sustain more severe injuries and different types of injury compared with their younger counterparts. AIMS: To examine the role of injury-related characteristics in explaining the impact of age on wage replacement duration, and whether the relationship between age and wage replacement duration is consistent across injury types and levels of severity. METHODS: A secondary analysis of workers' compensation claims in the Australian state of Victoria. In Victoria, only injuries which have accumulated >10 days of wage replacement, or have health care expenditures above a financial threshold, are eligible for compensation. Nested regression models were used to examine the relative contribution of injury-related characteristics to age differences in wage replacement duration. RESULTS: Older age was associated with greater days of wage replacement among men and women, even after adjusting for injury characteristics. Adjustment for differences in injury types and compensation reporting practices resulted in moderate attenuation of the age-duration relationship among men and small attenuation among women. The age-duration relationship was consistent across injury types/severity. CONCLUSIONS: The relationship between older age and greater duration of wage replacement is ubiquitous across injuries of different types and severity. Future research is required to understand better why older age is consistently associated with worse compensation outcomes following work-related 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".