Cannabis-related driving and passenger behaviours among high school students: a cross-sectional study using survey data
Bibliographic record
Abstract
BACKGROUND: Many youth report driving under the influence of cannabis (DUIC) and riding with a cannabis-impaired driver (RWCD), and many perceive that cannabis causes limited impairment. We examined associations of perceived risk of regular cannabis use with DUIC and RWCD, exploring differences by sex and rural setting. METHODS: In a cross-sectional study, we examined DUIC and RWCD among high school students in grades 11 and 12 who participated in the 2016-2017 Canadian Student Tobacco, Alcohol and Drugs Survey. Private and public schools across 9 Canadian provinces were included. New Brunswick and the 3 territories were not included. Multinomial logistic regression models generated adjusted and unadjusted models for the associations. RESULTS: = 52 103/68 415). In total, 14 520 students in grades 11 and 12 participated in the survey. Greater perceived risk of regular cannabis use was associated with reduced risk of DUIC and RWCD in a dose-response manner. Students perceiving that regular cannabis use posed great risk had an adjusted relative risk (RR) of 0.06 (95% confidence interval [CI] 0.04-0.10) of DUIC in the past 30 days compared with students perceiving that regular use posed no risk. Students perceiving that regular cannabis use posed great risk had an adjusted RR of 0.09 (95% CI 0.07-0.12) of RWCD in the past 30 days compared with students perceiving no such risk. Associations were consistent for male and female students and for those living in urban and rural areas. INTERPRETATION: Students perceiving minimal risk from cannabis use reported greater engagement in cannabis-related risky driving behaviours. Given the importance of youth perceptions in shaping driving and passenger behaviours, efforts must be made to disseminate appropriate information regarding cannabis-related driving risks to high school students.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".