0085 Injury reporting, employer lodgement and compensation payment delays and duration of wage replacement in injured workers
Bibliographic record
Abstract
Objective To determine if delays in the workers’ compensation process, indicated by failures in claim filing, adjudication and provision of wage replacement (WR), are associated with poorer RTW outcomes. Methods This study examined standard workers’ compensation claims with an injury date between January 2007 and December 2012, with at least one-day of WR, and which were not terminated for reasons other than RTW within the first 12 months of the claim (n=80,322). Logistic regression models explored the association between: i) delays in the injured workers (IWs) claim lodgement, the IWs employer’s lodgement of the claim with the insurer, and receipt of first compensation payment, and accumulating 52 weeks of WR; and ii) socio-demographic/economic, occupational, and injury-related factors and the aforementioned delays. Results All delays were associated with increased odds of reaching 52 weeks of WR. The more delays, the greater odds of a long-term claim. Different factors were associated with each different delay. Conclusions The predictive ability of delays in claim lodgement and processing and receipt of compensation payments demonstrate where improved claims management and adjudication could reduce the proportion of workers on long term WR.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".