The association between state cannabis policies and cannabis use among adults and youth, United States, 2002–2019
Bibliographic record
Abstract
AIMS: To measure the association between state cannabis policies and use among adults and youth in the United States from 2002 to 2019, given rapid policy liberalization and complex state cannabis policy environments. DESIGN: Repeated cross-sectional time series analysis. Three sets of models assessed the linear association between the Cannabis Policy Scale (CPS), an aggregate measure of 17 state cannabis policy areas that weights each policy by its efficacy and implementation rating, and prevalence of cannabis use. The first included year and state fixed effects; the second added state-level controls; the third replaced state fixed effects with state random effects. Standard errors were clustered at the state level in all models. SETTING AND PARTICIPANTS: United States. MEASUREMENTS: Past-month prevalence of cannabis use is from the National Survey on Drug Use and Health Small Area Estimates, a nationally and state-representative cross-sectional survey of household population ages 12 and older for years 2002-2003 to 2018-2019. Exposure data include the CPS. FINDINGS: A 10 percentage-point increase in the CPS (i.e. greater cannabis policy restrictiveness) was associated with lower past-month use prevalence by 0.81 (95% confidence interval [CI] = -1.05 to -0.56) to 0.97 (95% CI = -1.19 to -0.75) percentage-points for the population ages 12 years and older. When models were stratified by age, a 10 percentage-point increase in the CPS was associated with a 0.87 (95% CI = -1.13 to -0.61) to 1.04 percentage-point (95% CI = -1.03 to -0.84) reduction in past-month use prevalence for adults ages 18 years and older, and a 0.17 (95% CI = -0.24 to -0.09) to 0.21 percentage-point (95% CI = -0.35 to -0.07) reduction for youth ages 12-17 years. CONCLUSIONS: More restrictive US cannabis policies appear to be associated with reduced cannabis use for both adults and youth.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".