Drinking, Substance Use and the Operation of Motor Vehicles by Young Adolescents in Canada
Bibliographic record
Abstract
BACKGROUND: Impaired driving is a recognized cause of major injury. Contemporary data are lacking on exposures to impaired driving behaviours and related injury among young adolescents, as well as inequities in these youth risk behaviours. METHODS AND FINDINGS: Cycle 6 (2009/10) of the Health Behaviour in School-Aged Children survey involved 26,078 students enrolled in 436 Canadian schools. We profiled cross-sectionally the reported use of alcohol, marijuana, or other illicit drugs by on-road and off-road vehicle operators when young adolescents (mean age 13.3 (± 1.6) years) were either driving or riding as a passenger. Comparisons were made across vulnerable subgroups. Multi-level logistic regression analyses were used to quantify the effects of the driving behaviours on risks for motor vehicle-related injury. Attributable risk fractions were also estimated. A total of 10% (± 3%) of participants reported recent operation of an on-road or off-road motor vehicle after consuming alcohol, marijuana, or other illicit drugs, while 21% (± 3%) reported riding as a passenger with a driver under the same conditions. Larger proportions of youth reporting these risk behaviours were males, and from older age groups, rural communities, and socio-economically disadvantaged populations. The behaviours were consistently associated with increased risks for motor vehicle-related injury at the individual level (RR 2.35; 95% CI: 1.54 to 3.58 for frequent vs. no exposure as a driver; RR 1.68; 95% CI: 1.20 to 2.36 for frequent vs. no exposure as a passenger) and at the population level (Attributable Risk Fraction: 7.1% for drivers; 14.0% for passengers). The study was limited mainly by its reliance on self-reported data. CONCLUSION: Impaired driving is an important health priority among young adolescents in Canada. Inequities in the involvement of younger adolescents in these risk behaviours suggest the need for targeted interventions for specific subgroups such as youth from rural communities, and among socially disadvantaged populations.
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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".