Risk Factors Associated With Driving After Cannabis Use Among Canadian Young Adults
Bibliographic record
Abstract
This study identifid the most prominent risk factors associated with driving after cannabis use (DACU). 1,126 Canadian drivers (17–35 years old) who have used cannabis in the past 12 months completed an online questionnaire about sociodemographic information, substance use habits, cannabis effect expectancies, driving behaviours and peers’ behaviours and attitudes concerning DACU. A hierarchical logistic regression allowed identifying variables that were associated with DACU. Income (CA$30,000–CA$69,000), weekly-to-daily cannabis use, higher level of cannabis-related problems, expectation that cannabis facilitates social interactions, drunk driving, belief that DACU is safe, general risky driving behaviours, having a few friends who had DACU and injunctive norms predicted past 12-month DACU. Older age, holding negative expectations concerning cannabis, driving aggressively and perceived accessibility of public transportation decreased the probability of DACU. With restricted resources, programmes will be more efficient by targeting Canadian young adults most inclined to DACU by focussing on these risk factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".