Exposure to cannabis marketing in the United States and differences by cannabis laws: Findings from the International Cannabis Policy Study
Bibliographic record
Abstract
BACKGROUND: A growing number of US states have legalized adult "recreational" cannabis; however, there is little evidence on the impact of cannabis policies on cannabis marketing exposure to date. The current study examined marketing exposure in the US, including differences between states where cannabis is illegal ('illegal' states), legal for medical use ('medical'), and legal for recreational use ('recreational'). METHODS: Data are from the US component of the International Cannabis Policy Study: national repeat cross-sectional data from surveys conducted with 187,573 respondents aged 16-65 over 6 annual survey waves (2018-2023). Adjusted mixed effects logistic regression (GLIMMIX) models examined differences in self-reported exposure to cannabis marketing ('noticing') by state-level cannabis laws. RESULTS: Self-reported exposure to cannabis marketing differed across policy changes. Noticing cannabis marketing was lowest in illegal states and increased in the first 12-months following medical legalization (35.4 % vs. 39.2 %: AOR=1.16; 95 % CI=1.01-1.33; p = 0.034). Noticing marketing was highest in 'recreational' states, with increases in the first 12-months following legalization (50.0 % vs. 41.1 %: AOR=1.41; 95 % CI=1.34-1.48; p < .001), and additional increases 1-3 years (56.2 %: AOR=1.20; 95 % CI=1.14-1.25; p < .001) and 4 or more years following legalization (63.9 %: AOR=1.21; 95 % CI=1.16-1.27; p < .001). Noticing was highest among people who consume cannabis and younger ages. CONCLUSIONS: Self-reported exposure to cannabis marketing increases following medical and recreational legalization and is disproportionately noticed by underaged people. Cannabis regulations in 'legal' markets should account for marketing, which has been shown to promote 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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".