Youth and Young Adults’ Knowledge and Perceptions of Risks and Benefits Regarding Cannabis Products: A Cross-Sectional Analysis of Over 1,700 Individuals
Bibliographic record
Abstract
Canada legalized the use of non-medical cannabis in 2018. This study examines youth and young adults’ knowledge and perceptions of harms, benefits, and education around cannabis use since legalization. An online survey was completed by a convenience sample of 1,759 individuals aged 12–25 years living in Manitoba, Canada. Most participants (n = 1,525, 86.7%) reported receiving education on the potential effects/harms related to cannabis; the most common topics included driving and cannabis use (79.9%), the mental harms of cannabis (67.4%), and addiction and dependency (66.3%). Youth who reported using cannabis more than once (n = 1,203) were more knowledgeable about the effects of cannabis than youth who never used cannabis or used cannabis once (n = 580; mean score: 6.6 versus 5.7 out of 8, respectively; p < .001). Vaping cannabis oil was perceived as the most harmful cannabis product among all participants. Among participants with experience using cannabis, the most frequently reported benefits were relaxation, improved sleep, and enhanced enjoyment of food/music. Half of the participants reported ever being in a car with someone driving high, of which, 40% of these participants reported doing so in the last 30 days. Future tailored education is needed to address knowledge related to cannabis use among youth and young adults who use and do not use cannabis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".