Exploring Youths’ Cannabis Health Literacy Post Legalization: A Qualitative Study
Bibliographic record
Abstract
Legalization of non-medical cannabis in Canada was intended to protect youth health and safety by limiting access and raising awareness of safety and risks. The purpose of this qualitative research was to explore youths’ perceptions of their cannabis health literacy and future educational needs. A convenience sample of youth aged 13 to 18 residing in Newfoundland and Labrador, Canada who may or may not have consumed cannabis were included. A qualitative study using virtual focus groups with semi-structured interview questions was conducted. Ethics approval was obtained. All sessions were audio-recorded and transcribed. Inductive thematic analysis used a social-ecological framework for adolescent health literacy. Six focus groups ( n = 38) were conducted with youth of all ages and from rural and urban areas. Three main themes were identified: (i) micro influences (age, gender, and beliefs), (ii) meso influences, (family, peers, and school enforcement), (iii) macro influences (cannabis legalization and social media), and (iv) evidence-informed information (harm reduction and cannabis properties). They desired evidence-informed education using harm-reduction principles, integrated early, and interactive. The findings provide support for a cannabis health literacy framework that will inform youth cannabis education programs. Interactive approaches with real-world application should support their autonomy, share knowledge, and minimize stigma.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".