Cannabis use among workers with work-related injuries and illnesses: results from a cross-sectional study of workers’ compensation claimants in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: Little is known about how workers use cannabis following a work-related injury/illness, including whether they receive clinical guidance. The objective was to compare characteristics of workers using and not using cannabis after a work-related injury/illness and describe use patterns. DESIGN: Cross-sectional study. SETTING AND PARTICIPANTS: Workers who experienced a work-related physical injury/illness resulting in one or more days of lost time compensated by the workers' compensation authority in Ontario, Canada (n=1196). METHODS: Participants were interviewed 18 or 36 months after their injury/illness. Participants were asked about their past-year cannabis use, including whether use was for the treatment of their work-related condition. Sociodemographic, work and health characteristics were compared across cannabis groups: no past-year use; use for the work-related condition; use unrelated to the work-related condition. Cannabis use reasons, patterns, perceived impact and healthcare provider engagement were described. RESULTS: In total, 27.4% of the sample reported using cannabis (14.1% for their work-related condition). Workers using cannabis for their condition were less likely to be working (58.0%) and more likely to have quite a bit/extreme pain interference (48.5%), psychological distress (26.0%) and sleep problems most/all the time (62.1%) compared with those not using cannabis (74.3%, 26.3%, 12.0% and 38.0%, respectively) and those using cannabis for other reasons (74.2%, 19.5%, 12.0% and 37.1%, respectively) (all p<0.0001). No significant differences were observed in medical authorisations for use among those using cannabis for their condition (20.4%) or unrelated to their condition (15.7%) (p=0.3021). Healthcare provider guidance was more common among those using cannabis for their condition (32.7%) compared with those using for other reasons (17.1%) (p=0.0024); however, two-thirds of this group did not receive guidance. CONCLUSIONS: Cannabis may be used to manage the consequences of work-related injuries/illnesses, yet most do not receive clinical guidance. It is important that healthcare providers speak with injured workers about their cannabis use.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".