Reducing the harms of cannabis use in youth post-legalization: insights from Ontario youth, parents, and service providers
Bibliographic record
Abstract
BACKGROUND: Canada has one of the highest prevalence of cannabis use globally, particularly among young adults aged 20-24 (50%) and youth aged 16-19 (37%). In 2018, Canada legalized recreational cannabis with the aim of protecting youth by restricting their access and raising public awareness of health risks. However, there has been limited qualitative research on the perceptions of harms associated with youth cannabis use since legalization, which is crucial for developing effective harm reduction strategies. This qualitative study examined perceptions of cannabis use among youth from the perspectives of youth, parents, and service providers. We explored how participants described the perceived risks or harms associated with youth cannabis use, as well as how they described their own and others' approaches to reducing cannabis-related risks and harms. METHODS: This qualitative study used a community-based participatory research approach in partnership with Families for Addiction Recovery (FAR), a national charity founded by parents of youth and young adults with addiction issues. Virtual semi-structured interviews were conducted, and the data were analyzed using thematic analysis. RESULTS: The study included 88 participants from three key groups (n = 31 youth, n = 26 parents, n = 31 service providers). Two main themes emerged regarding perceived risks or harms associated with cannabis use: (1) concerns about cannabis-related risks and harms, including addiction, brain development, impact on family, and various adverse effects on areas such as motivation, concentration, finances, employment, education, physical and mental health; and (2) minimization of risks and harms, featuring conflicting messages, normalization, and perceptions of cannabis being less harmful than other substances. Additionally, two themes related to harm reduction approaches were identified: (1) implementation of harm reduction, and (2) challenges in implementing a harm reduction approach. Specific challenges for each participant group were noted, along with structural barriers such as unavailable and inaccessible services, easy access to cannabis, inadequate public education, and insufficient information on lower-risk cannabis use guidelines. CONCLUSIONS: Youth cannabis use is a significant public health concern that requires a multi-pronged approach. Developing youth-centered harm reduction strategies that recognize the developmental needs and vulnerabilities of youth, as well as the important role of families, is imperative.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".