Occupational patterns of opioid-related harms comparing a cohort of formerly injured workers to the general population in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: The role of work-related injuries as a risk factor for opioid-related harms has been hypothesized, but little data exist to support this relationship. The objective was to compare the incidence of opioid-related harms among a cohort of formerly injured workers to the general population in Ontario, Canada. METHODS: Workers' compensation claimants (1983-2019) were linked to emergency department (ED) and hospitalization records (2006-2020). Incident rates of opioid-related poisonings and mental and behavioural disorders were estimated among 1.7 million workers and in the general population. Standardized incidence ratios (SIRs) and 95% confidence intervals (CI) were calculated, adjusting for age, sex, year, and region. RESULTS: Compared to the general population, opioid-related poisonings among this group of formerly injured workers were elevated in both ED (SIR = 2.41, 95% CI = 2.37-2.45) and hospitalization records (SIR = 1.54, 95% CI = 1.50-1.59). Opioid-related mental and behavioural disorders were also elevated compared to the general population (ED visits: SIR = 1.86, 95% CI = 1.83-1.89; hospitalizations: SIR = 1.42, 95% CI = 1.38-1.47). Most occupations and industries had higher risks of harm compared to the general population, particularly construction, materials handling, processing (mineral, metal, chemical), and machining and related occupations. Teaching occupations displayed decreased risks of harm. CONCLUSION: Findings support the hypothesis that work-related injuries have a role as a preventable risk factor for opioid-related harms. Strategies aimed at primary prevention of occupational injuries and secondary prevention of work disability and long-term opioid use are 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".