Relationships of Changing State Cannabis Policies With Alcohol Policy Effectiveness and Alcohol or Cannabis Involvement in Motor Vehicle Fatalities
Bibliographic record
Abstract
INTRODUCTION: Alcohol use is an established and important risk factor for motor vehicle crashes and crash fatalities. The liberalization of cannabis policy across U.S. states could impact motor vehicle crash fatalities involving alcohol or the co-use of alcohol and cannabis. METHODS: Mortality data were from the Fatality Analysis Reporting System in 50 states and Washington, DC, from 2010 to 2019. State-year alcohol policy scores and cannabis policy scores were used as measures of policy exposure in multivariable mixed logistic regression models to estimate the AOR of 2 blood alcohol concentration thresholds and/or any detectable tetrahydrocannabinol involvement in crash fatalities. RESULTS: In fully adjusted models, a 10-percentage point increase in alcohol policy scores (representing more robust alcohol control policies) was associated with a 6.3% lower risk of a blood alcohol concentration >0.00% (AOR=0.937; 95% CI=0.886, 0.991) or involvement at a blood alcohol concentration ≥0.08% (AOR=0.938; 95% CI=0.888, 0.992) among motor vehicle crash decedents. However, there were no significant independent association between cannabis policy scores and alcohol involvement. A 10-percentage point increase in cannabis policy scores (representing more robust cannabis control policies) was associated with reduced odds of cannabis involvement (AOR=0.956; 95% CI=0.922, 0.991) or alcohol and cannabis coinvolvement (AOR=0.962; 95% CI=0.928, 0.997). CONCLUSIONS: More restrictive alcohol policies and cannabis policies were associated with reduced odds of motor vehicle crash fatalities involving alcohol and cannabis, respectively. Cannabis policies did not affect protective associations between alcohol policies and alcohol involvement. However, more restrictive cannabis policies were protective for coinvolvement of alcohol and cannabis.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".