Health Care Provider Communication and the Duration of Time Loss Among Injured Workers
Bibliographic record
Abstract
BACKGROUND: In addition to providing injured workers with biomedical treatment, health care providers (HCPs) can promote return to work (RTW) through various communications. OBJECTIVES: To test the effect of several types of HCP communications on time loss following injury. RESEARCH DESIGN: The authors analyzed survey and administrative claims data from a total of 730 injured workers in Victoria, Australia. Survey responses were collected around 5 months postinjury and provided data on HCP communication and confounders. Administrative claim records provided data on compensated time loss postsurvey. The authors conducted multivariate zero-inflated Poisson regressions to determine both the odds of having future time loss and its duration. MEASURES: Types of HCP communications included providing an estimated RTW date, discussing types of activities the injured worker could do or ways to prevent a recurrence, and contacting other RTW stakeholders. Each was measured in isolation as well as modified by a low-stress experience with the HCP. Time loss was the count of cumulative compensated work absence in weeks, accrued postsurvey. RESULTS: RTW dates reduced the odds of future time loss [odds ratio, 0.26; 95% confidence interval (CI), 0.09-0.82] regardless of the stressfulness of the experience. Communications that predicted shorter durations of time loss only did so with low-stress experiences: RTW date [incidence rate ratio (IRR), 0.56; 95% CI, 0.50-0.63], stakeholder contact (IRR, 0.78; 95% CI, 0.70-0.87), and prevention discussions (IRR, 0.87; 95% CI, 0.78-0.98). CONCLUSIONS: HCPs may reduce time loss through several types of communication, particularly when stress is minimized. RTW dates had the largest and 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".