O21-1 The interplay between workplace factors and health care providers on return to work among workers’ compensation claimants in victoria, australia
Bibliographic record
Abstract
This study examined the interplay between workplace factors (contact from the workplace RTW coordinator and the supervisor reaction to the injury); and health care provider (HCP) factors (HCP contact with the workplace and the HCP giving a return to work date) and RTW. We did this using a longitudinal cohort of workers’ compensation claimants with psychological and musculoskeletal injuries from Victoria Australia. Workplace and HCP factors were measured as part of a survey administered to 869 claimants, as soon as possible after their claim was accepted. RTW was assessed using self-report in a follow-up survey administered approximately 6 months after the baseline survey (response rate to the 6-month survey was 75%). The study sample was workers who were off work at the baseline survey, who responded to the 6-month follow-up survey (N = 260). Workplace factors were related. When the respondent’s supervisor had a positive reaction to their injury, 68% had also been contacted by their RTW coordinator. Conversely, when the supervisor reaction was not positive, only 41% had been contacted. HCP factors also overlapped. When the HCP was in contact with the workplace, 29% of the time they had given a RTW date. When the HCP was not in contact with the workplace they had given a RTW date 14% of the time. Over the 6-month period 46% of the sample returned to work. Workplace factors had additive contributions on likelihood of RTW. Claimants with a positive supervisor reaction and RTW coordinator contact had an odds of returning to work of 3.55 (95% CI: 1.60–7.90), compared to claimants with a supervisor reaction that was not positive and no contact from their RTW coordinator. The relationship between HCP factors was less clear. Examinations of workplace and HCP factors on RTW should consider interplay between these factors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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 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".