Canadian cannabis education resources to support youth health literacy: A scoping review and environmental scan
Bibliographic record
Abstract
Background: The 2018 legalisation of cannabis in Canada sparked concern and conversation about the potential negative impacts of youth cannabis use. It is clear that young people are already engaging in cannabis use for a variety of reasons; therefore, youth cannabis education is desirable to promote harm reduction and reduce the risk of adverse physical and mental health outcomes. Objective: To identify and categorise Canadian cannabis education resources using a social-ecological approach informed by the youth health literacy framework, considering multiple factors at the micro-, meso- and macro-levels that influence health literacy and impact behaviour. Methods: In line with scoping review methodology, database searches and an environmental scan of materials were completed. Specific inclusion criteria were identified to encompass all Canadian cannabis education resources directed towards young people aged 9-18 years and adults in contact with youth. Results: A total of 60 resources were identified and categorised using the youth health literacy framework in terms of their focus on (1) micro influences (resources for youth); (2) meso influences (resources for teachers, parents, mentors); and (3) macro influences (resources for indigenous communities and medical professionals). Conclusions: While many resources were identified, issues exist with the accessibility, quality and multicultural considerations of such resources, warranting the development of comprehensive, evidence-based and harm reduction-focused cannabis education for youth.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.023 | 0.033 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".