Risk Perceptions Related to Driving After Use of Alcohol and Cannabis in a Cross-National Sample of University Students in 6 Countries
Bibliographic record
Abstract
BACKGROUND: Cross-national comparisons of driving under the influence (DUI) of cannabis (DUIC) or DUI of alcohol (DUIA; ie, driving after consuming too much to drive safely) may inform policies and preventative measures, although research is currently limited. This cross-national study sought to compare the frequency of DUI, being a passenger with an impaired driver, and associated risk perceptions. METHODS: Secondary analyses of students from 6 countries (Argentina, Canada, England, Spain, South Africa, United States). Participants (n = 5167; 70% women; mean age 20.1 [SD = 3.7]) completed an online survey assessing past-year frequency of alcohol or cannabis-impaired driving and being a passenger with an impaired driver. Risk perceptions included the perceived threat to personal safety of impaired driving, and perceived likelihood of negative consequences (eg, being in an accident, stopped by police). Differences across countries were tested using chi-square tests with Bonferroni-corrected adjusted residuals for pairwise comparisons. RESULTS: Endorsement of impaired driving was generally low (<12%) across countries. Significant differences were found across countries in perceived threat to the safety of driving after using alcohol or cannabis. Compared to other countries, England and Spain rated DUIA as less of a threat, and Argentina rated DUIC as less of a threat. Perceived likelihood of consequences also differed across countries, potentially due to perceptions of reduced enforcement in some countries (eg, Argentina, South Africa). Finally, participants with a history of impaired driving and men in some countries were more likely to report more favorable risk perceptions (ie, lower threat and lower likelihood of consequences) than drivers who reported never driving impaired. CONCLUSIONS: These results offer preliminary evidence of cross-national differences in alcohol and cannabis impaired driving and associated risk perceptions, providing a foundation for future studies investigating causal factors such as legalization and enforcement of driving-related laws across countries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".