Differences in patterns of cannabis use among youth: Prevalence, perceptions of harm and driving under the influence in the USA where non‐medical cannabis markets have been established, proposed and prohibited
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Cannabis use is the most widely used illicit substance in the USA. Currently, over half of US jurisdictions have legalised medical cannabis and nine US jurisdictions (and Washington DC) have legalised non-medical cannabis. Comparisons across jurisdictions can help to evaluate the impact of these policies. The current study examined patterns of cannabis use among youth in three categories: (i) states that have legalised non-medical cannabis with established markets; (ii) jurisdictions that recently legalised non-medical cannabis without established markets; and (iii) all other jurisdictions where non-medical cannabis is prohibited. DESIGN AND METHODS: Data come from an online survey conducted among 4097 US youth aged 16-19 recruited through a commercial panel in July/August 2017. Regression models were fitted to examine differences between regulatory categories for cannabis consumption, perceived access to cannabis, modes of use, perceptions of harm and cannabis-impaired driving. All estimates represent weighted data. RESULTS: States that had legalised non-medical cannabis had higher prevalence, easier access and lower driving rates than non-legal states. There were few differences between states with established non-medical cannabis markets and those that had recently legalised. DISCUSSION AND CONCLUSIONS: Cannabis use among youth was higher in states that have legalised non-medical cannabis, regardless of how long the policy had been implemented or whether markets had been established. This suggests that differences between states with and without legal non-medical cannabis may partly be due to longer-term patterns established prior and highlights the importance of longitudinal evidence to evaluate the impact of 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".