Cannabis use and work-related injuries: a cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Although the association of cannabis use with automobile accidents has been well-studied, the impact of cannabis on workplace safety and injuries is less clear. AIMS: The purpose of this study was to examine the relationship between work-related injury and cannabis use in the past year. METHODS: We performed a cross-sectional analysis of the Canadian Community Health Survey (2013-16) of working individuals. We used multiple logistic regression modelling to calculate the odds of experiencing a work-related injury (defined as non-repetitive strain injury) among workers who reported using cannabis more than once during the prior 12 months as compared to non-users. We repeated the analysis among participants working in high injury risk occupational groups only. RESULTS: Among the 136 536 working participants, 2577 (2%) had a work-related injury in the last 12 months. Of these 2577 who had a work-related injury, 4% also reported being a cannabis user in the same period. We found no association between past-year cannabis use and work-related injury (odds ratio for work injury among users 0.81, 95% confidence interval 0.66-0.99). The association was unchanged in the subgroup analysis limited to high injury risk occupational groups. CONCLUSIONS: We found no evidence that cannabis users experienced higher rates of work-related injuries. While awaiting prospective studies, occupational medicine practitioners should take a risk-based approach to drafting workplace cannabis policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".