How much can you drink before driving? The influence of riding with impaired adults and peers on the driving behaviors of urban and rural youth
Bibliographic record
Abstract
AIMS: Following an ecological model to specify risks for impaired driving, we assessed the effects of youth attitudes about substance use and their experiences of riding in cars with adults and peers who drove after drinking alcohol or smoking cannabis on the youths' own driving after drinking or using cannabis. DESIGN AND METHODS: Participants were 2594 students in grades 10 and 12 (mean age = 16 years and 2 months; 50% girls) from public high schools in urban (994) and rural communities (1600) on Vancouver Island in British Columbia, Canada; 1192 of these were new drivers with restricted licenses. Self-report data were collected in anonymous questionnaires. Regression analyses were used to assess the independent and interacting effects of youth attitudes about substance use and their experiences of riding in cars with adults or peers who drove after drinking alcohol or smoking cannabis on youth driving. FINDINGS: Youth driving risk behaviors were associated independently with their own high-risk attitudes and experiences riding with peers who drink alcohol or use cannabis and drive. However, risks were highest for the youth who also report more frequent experiences of riding with adults who drink alcohol or use cannabis and drive. CONCLUSIONS: Prevention efforts should be expanded to include the adults and peers who are role models for new drivers and to increase youths' awareness of their own responsibilities for their personal safety as passengers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".