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 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 | 0.003 |
| 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 | 0.000 |
| 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".