Marijuana trajectories and associations with driving risk behaviors in Canadian youth
Bibliographic record
Abstract
Objective: Research on risky driving practices involving marijuana use among youth and young adults often relies on cross-sectional data, which fail to account for longitudinal changes in substance use patterns. A better understanding of the longitudinal patterns of marijuana use and its effect on risky driving practices during young adulthood is needed in order to better inform prevention efforts. The current study examined whether different longitudinal patterns of marijuana use across the transition from adolescence to young adulthood are associated with impaired driving risks in young adulthood. Methods: Data were from the longitudinal Victoria Healthy Youth Survey, which interviewed youth biennially on 6 occasions across 10 years (2003 to 2013). Results: Youth who reported consistently high levels of marijuana use from adolescence to young adulthood (chronic users) and youth who reported increasing levels of use across this period (increasers) were more likely to engage in risky impaired driving behaviors compared to the other 3 user groups (occasional users, decreasers, and abstainers). Frequency of marijuana use was also predictive of impaired driving risks in young adulthood after controlling for individual characteristics (age, sex, socioeconomic status, age of onset of marijuana use), frequency of other substance use (heavy episodic drinking and illicit drug use), and simultaneous use of marijuana and other substances (alcohol and illicit drugs). By young adulthood, youth who use marijuana more than once a week are more likely to simultaneously use alcohol and engage in heavy episodic drinking. They are also more likely take driving risks. Conclusions: Harm reduction strategies and legislative approaches targeting impaired driving risks associated with marijuana use should include approaches to target these high-risk groups and to reduce simultaneous use of alcohol.
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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 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".