Opioid use among injured workers: pain and the return-to-work experience
Bibliographic record
Abstract
OBJECTIVE: In this cross-sectional analysis, we explored how return-to-work (RTW) experiences and postinjury pain are associated with opioid use after a workplace injury/illness. METHODS: Workers with accepted lost-time claims, compensated by the workers' compensation board in Ontario, Canada were interviewed by telephone 18 months following a work-related physical injury/illness. Participants were asked about their past-year opioid use, current pain, RTW timing and workplace accommodations. Separate logistic regression analyses were conducted to estimate the association between two independent variables and opioid use: one combining the presence of pain with workplace accommodation and a second combining the presence of pain with RTW timing, adjusted for sociodemographic, work, injury and health covariates. RESULTS: Of 1793 participants included in the analysis, 35.6% used opioids more than once in the past 12 months. Compared with those who did not return to work too soon and had no/mild pain, odds of opioid use were higher among those with severe pain, both those who returned too soon (OR 2.90, 95% CI 2.11 to 3.99) and those who did not return too soon (OR 3.01, 95% CI 2.16 to 4.19). Compared with those who had an offer of accommodation and no/mild pain, workers with severe pain and an accommodation offer (OR 2.78, 95% CI 2.16 to 3.57) or without an offer (OR 2.69, 95% CI 1.90 to 3.81) had increased odds of reporting use of opioids. CONCLUSIONS: Findings suggest pain is the main factor associated with opioid use after a work-related injury, irrespective of RTW experiences. However, due to the limitations of this exploratory analysis, longitudinal research examining this issue is warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".