Driving under the influence of cannabis: perceptions from Canadian youth
Bibliographic record
Abstract
BACKGROUND: Cannabis legalization is intended to protect the public from potential harm by restricting access and promoting greater awareness of cannabis-related risks. Youth are at a greater risk for experiencing road-related harms due to their own or others' use of cannabis. This qualitative research explored youths' perceptions about cannabis and road safety. METHODS: A qualitative study using focus groups (FG) was conducted with youth (age 13-18) and young adults (age 19-25) who resided in Newfoundland and Labrador. Using semi-structured interview questions, the facilitator asked participants to share their opinions about cannabis and road safety. All sessions were hosted virtually using Zoom with recruitment until saturation was met. All sessions were audio recorded, de-identified, and transcribed. Analysis utilized an inductive thematic approach informed by Braun and Clarke's (2006) method and inductive coding was facilitated using NVivo. RESULTS: Six youth (n = 38) and five young adult (n = 53) FG were conducted. Five prominent themes emerged throughout discussions across both age groups including: a) normalization of driving under the influence of cannabis, b) knowledge and awareness, c) perceptions of risk, d) modes of transportation, and e) detection. Variation in perceptions appeared to be influenced by lack of awareness of the impact of cannabis on driving ability, residence in urban versus rural locations, type of vehicle driven (e.g., car vs. off-road vehicles), and gender. CONCLUSION: The themes uncovered from this research will help inform future enhancement of cannabis policy to ensure the safety of all citizens. These findings will also support the inclusion of youth-focused education that will equip youth with informed decision-making strategies regarding road safety. Furthermore, these findings can be utilized to inform the refinement of cannabis driving policies to ensure the safety of all citizens on or off the road.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".