Perspectives on cannabis risks and harm reduction among youth in Early Psychosis Intervention programs: a qualitative study
Bibliographic record
Abstract
Purpose The Canadian government legalized cannabis in 2018 and funded harm reduction campaigns to educate youth about the risks. Cannabis can contribute to psychosis in vulnerable populations, and consumption is common among youth in Early Psychosis Intervention (EPI) programs. The purpose of this study is to understand the views of youth in EPI programs on the risks related to cannabis and methods to reduce those risks. Design/methodology/approach A qualitative design and thematic analysis were used to understand the perspectives of youth in EPI programs ( n = 15) towards cannabis risks and harm reduction. Findings Participants associated Δ−9 tetrahydrocannabinol (THC) with problems related to cognition, psychosis, respiration, addiction, motivation, finances, relationships and anxiety. Cannabidiol (CBD) was believed to be safe and risk-free. To reduce the risks associated with THC, participants suggested using in moderation, delaying use, using CBD over THC, accessing legal sources, avoiding high THC dosages and using non-combustible methods. Research limitations/implications Participants self-selected to participate, were psychiatrically stable and may not represent youth in EPI programs with more severe psychotic symptoms. Practical implications Assessing risk perceptions, motives for use and perspectives towards the cannabis and psychosis connection can reveal educational needs. CBD may offer a harm reduction option for EPI clients wanting to decrease THC intake, though more research is needed and adverse effects should be explained. Educational campaigns should disseminate the connection between cannabis and psychosis to facilitate early intervention. Originality/value This study adds to the literature by highlighting knowledge of harm reduction methods and gaps in risk awareness among EPI program youth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".