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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".