P.2.10 Healthcare provider communication and the duration of time off work among injured workers: a prospective cohort study
Bibliographic record
Abstract
Background In addition to biomedical treatment, healthcare providers (HCPs) may make psychosocial contributions to injured workers that aide rehabilitation and the return to work (RTW) process. We examined the effect on disability duration of several types of HCP communications with injured workers and stakeholders in the RTW process. Objectives To test the effect of various HCP communications on time off work following injury. Research design We analysed survey and administrative claims data from n=715 injured workers in Victoria, Australia. Survey responses were collected around five months post-injury and provided data on HCP communication and confounders. Administrative claims data provided data on compensated time off work. We conducted multivariate zero-inflated Poisson regression analyses, which evaluated both the likelihood of future time off work and its duration. Measures HCP communications included good interactions, estimated RTW date, activity discussions, prevention discussions, and stakeholder contact. Time off work was the count of cumulative compensated work absence in weeks, accrued post-survey. Results Only RTW dates were predictive of no future time loss (OR: 2.65, 95% CI: 1.74–4.03). RTW date (IRR: 0.71, 0.67–0.74), good interactions (IRR: 0.73, 0.70–0.76), and stakeholder contact (IRR: 0.92, 0.88–0.95) reduced time off work, while activity discussions predicted more time off work (IRR: 1.13, 1.08–1.19). Conclusions HCPs may be able shorten disability durations through several types of communication. Of those evaluated in this study, RTW dates had the most robust effect.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.005 |
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".