Adolescent passengers of drunk drivers: a multi‐level exploration into the inequities of risk and safety
Bibliographic record
Abstract
AIMS: This study determined the individual-, neighbourhood- and provincial-level effects of rural residence, socio-economic status (SES), substance use and driving behaviours on adolescents' riding with a drunk driver (RDD). DESIGN: Multi-level study based on cross-sectional self-reported anonymous data from the Student Drug Use Survey in the Atlantic Provinces (SDUSAP) and Census Canada data, merged on the postal code of participating schools. The sample design of the SDUSAP was a single-stage cluster sample of randomly selected classes stratified by grade and region. SETTING: The Atlantic provinces of Canada. PARTICIPANTS: A total of 12 990 students in junior and senior high schools, with an average age of 15 years, participated in the 2002 SDUSAP. MEASUREMENTS: The outcome variable was past-year RDD. The main individual-level independent variables were SES, rural residence, substance use and driving behaviours. The school-neighbourhood independent variables were the prevalence of heavy episodic drinking, driving under the influence of alcohol, driver's licence, highest level of educational attainment and low income. FINDINGS: The prevalence of RDD was 23.3% in 2002. Among students in grades 9-12, lower family SES, rural residence, substance use and driving under the influence were found to be independent individual-level risk factors for RDD; having a driver's licence was found to be protective. At the provincial and school-neighbourhood levels, a high prevalence of driving under the influence of alcohol and low educational attainment were found to be independent risk factors for RDD after taking into account individual characteristics. CONCLUSIONS: This study provides evidence that inequities exist in the options for adolescents to be ensured of passenger safety, and that interventions aimed at decreasing the extent to which adolescents engage in riding with a drunk driver should be based on conceptual approaches that recognize ecological factors as well as individual-level susceptibility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".