O2C.3 What predicts a secondary absence following return to work among workers’ compensation claimants in victoria? Results from a longitudinal cohort
Bibliographic record
Abstract
Time taken to first return to work (RTW) is often a primary endpoint for studies among injured workers. However, studies using administrative workers’ compensation claims data have documented that a substantial proportion (approximately one half) of claimants will incur a subsequent period of wage replacement. Unfortunately, workers’ compensation data is limited in the information collected to better understand which claimants are more likely to have a subsequent absence from work. The objective of this study is to address this gap using a cohort of workers’ compensation claimants in the Australian state of Victoria. The sample for this study is drawn from a longitudinal cohort of workers’ compensation claimants (n=869). For the purpose of this analysis we focused on those claimants who had returned to work (self-reported) at the baseline interview, which was conducted approximately 4 months after the injury had occurred (n=372). Independent variables examined included if the respondent was working on full or partial duties, currently receiving health care for their injury, type of injury (musculoskeletal versus psychological), co-worker responses when they returned to work (measured using nine questions), and work limitations, measured using an abbreviated form of the work limitations questionnaire. A total of 205 respondents (55% of the sample) reported a subsequent absence from work when interviewed 6 months later. All independent variables, with the exception of injury type, were associated with subsequent absences from work. In a multivariable model, only working modified duties and greater limitations remained statistically significant. The results of the current study help inform our understanding of trajectories in RTW and factors, measured after the first RTW, which may be associated with a subsequent absence from work. These findings can be integrated into RTW programs to help more workers achieve sustainable RTW following a work injury.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".