Alcohol policies and impaired driving in the United States: Effects of driving- vs. drinking-oriented policies
Bibliographic record
Abstract
AIMS: To test the hypotheses that stronger policy environments are associated with less impaired driving and that driving-oriented and drinking-oriented policy subgroups are independently associated with impaired driving. DESIGN: State-level data on 29 policies in 50 states from 2001-2009 were used as lagged exposures in generalized linear regression models to predict self-reported impaired driving. SETTING: Fifty United States and Washington, D.C. PARTICIPANTS: A total of 1,292,245 adults (≥ 18 years old) biennially from 2002-2010. MEASURES: Alcohol Policy Scale scores representing the alcohol policy environment were created by summing policies weighted by their efficacy and degree of implementation by state-year. Past-30-day alcohol-impaired driving from 2002-2010 was obtained from the Behavioral Risk Factor Surveillance System surveys. FINDINGS: Higher Alcohol Policy Scale scores are strongly associated with lower state-level prevalence and individual-level risk of impaired driving. After accounting for driving-oriented policies, drinking-oriented policies had a robust independent association with reduced likelihood of impaired driving. Reduced binge drinking mediates the relationship between drinking-oriented policies and impaired driving, and driving-oriented policies reduce the likelihood of impaired driving among binge drinkers. CONCLUSIONS: Efforts to reduce alcohol-impaired driving should focus on reducing excessive drinking in addition to preventing driving among those who are impaired.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".