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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".