Frequent marijuana use and driving risk behaviours in Canadian youth
Bibliographic record
Abstract
BACKGROUND: A better understanding of the relations between patterns of marijuana use and driving risks in young adulthood is needed. METHODS: Secondary analyses of self-report data from the Victoria Healthy Youth Survey. Youth (baseline ages 12 to 18; N=662; 52% females) were interviewed biannually (on six occasions) from 2003 to 2013 and classified as abstainers (i.e., used no marijuana in past 12 months), occasional users (i.e., used at most once per week), and frequent users (i.e., used more than once a week). RESULTS: In the frequent user group, 80% of males and 75% of females reported 'being in a car driven by driver (including themselves) using marijuana or other drugs in the last 30 days', 64% of males and 33% of females reported that they were 'intoxicated' with marijuana while operating a vehicle and 50% of males and 42% of females reported being in a car driven by a driver using alcohol. In addition, 28% of occasional users and also a small proportion of abstainers reported 'being in a car driven by a driver using marijuana or other drugs in the last 30 days'. INTERPRETATION: The high frequency of driving risk behaviours, particularly for frequent users, suggest that plans for legalization of recreational use should anticipate the costs of preventive education efforts that present an accurate picture of potential risks for driving. Youth also need to understand risks for dependence, and screening for and treatment of marijuana use disorders is needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".