“Stoned on the road”: A systematic review of cannabis-impaired driving educational initiatives targeting young drivers in Canada
Bibliographic record
Abstract
BACKGROUND: With recreational cannabis legalized across Canada, concerns persist about youth driving under the influence of cannabis (DUIC). However, the extent of DUIC education and prevention efforts aimed at young Canadians remains unclear. This systematic review examines recent Canadian initiatives (2017 onwards) focused on reducing DUIC among youth. Specifically, we investigate (1) the types of initiatives and target audiences, (2) content and delivery methods, (3) sustainability, and (4) evidence of impact. METHODS: A comprehensive search was conducted across MEDLINE, PsycINFO, CINAHL, SCOPUS, and EMBASE (January 1, 2017-July 10, 2023), along with various grey literature sources. Initiatives were included if they targeted DUIC behaviour among youth aged 16 to 24, were developed and delivered in Canada by reputable organizations or individuals with institutional support, and aimed to address DUIC behaviour or its enabling conditions. Data extraction and quality appraisal were performed. RESULTS: Fifteen Canadian initiatives were identified: seven educational programs and eight awareness campaigns, encompassing national and regional levels. Delivery methods included in-person workshops, digital tools, online programs, and smartphone applications. While some initiatives increased awareness and influenced perceptions of DUIC, evidence of behaviour change remained limited. Challenges related to sustainability, particularly concerning long-term funding and digital platform maintenance, were noted. CONCLUSIONS: This research highlights the progress made in addressing youth DUIC in Canada. Examining current DUIC educational initiatives is crucial for refining strategies, shaping policy, and allocating resources to prioritize the safety of young Canadians. Future efforts should focus on assessing behavioural impacts and ensuring financial sustainability and program longevity.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.018 | 0.030 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".