Cross-sectional analysis of cannabis use at work in the USA: differences by occupational risk level and state-level cannabis laws
Bibliographic record
Abstract
Objective: To examine the prevalence of workplace cannabis use, including by state-level cannabis laws, occupational risk and medical cannabis use. Methods: Data are cross-sectional from wave 6 (2023) of the International Cannabis Policy Study (ICPS) and include 26 458 respondents aged 16-65 years from the USA. Separate regression models were run analysing workplace cannabis use across: (1) state-level cannabis laws and occupational risk, (2) reasons for cannabis use and (3) medical cannabis authorisation. All models were adjusted for sociodemographic characteristics. Results: Overall, 7.4% of workers and 21.5% of past 12-month cannabis consumers reported using cannabis at or within 2 hours of starting work in the last 30 days. Workplace cannabis consumption was highest among workers in states with 'recreational' cannabis laws (8.5%) compared with states with medical (6.3%; adjusted OR (AOR)=1.45, p=0.006) or illegal laws (6.2%; AOR=1.06, p=0.005). Workers in high-risk jobs were more likely to use cannabis at work (11.4%) than those in lower risk jobs (5.8%; AOR=1.58, p<0.001). Workplace cannabis use was also greater among cannabis consumers who use cannabis for medical versus recreational (29.4% vs 15.6%; AOR=2.35, p<0.001) or mixed reasons (24.2%; AOR=1.78, p=0.007); the same was true for consumers who reported having medical cannabis authorisation (39.0%) versus those without authorisation (17.4%; AOR=2.66, p<0.001). Conclusions: Reported cannabis use at work was most prevalent in states with recreational legalisation, particularly among individuals with medical cannabis authorisation and those who work higher risk jobs. Longitudinal research should examine the individual and occupational-level factors associated with workplace cannabis use.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".