Navigating Mental Health and Cannabis Use Post-cannabis Legalization: Experiences from Racialized Community Members
Bibliographic record
Abstract
Through conversations with members of racialized communities, we aimed to: explore perspectives shared by members of racialized communities regarding the relationship between cannabis and mental health; develop a greater understanding of racialized community members’ mental health service interactions in relation to cannabis use and mental health; and examine whether various social identities interact to influence experiences with cannabis use and mental illness. From January to June 2022, we conducted semi-structured interviews with 26 members of racialized populations who were ≥18 years old, had used cannabis in the last 6 months, and had been in contact with the mental health sector across Ontario in the past year for a known psychiatric diagnosis. Many participants were 25–34 (46%), Canadian citizens (89%), and heterosexual (50%), with representation from ten different ethno-racial identities. Seventy-three percent of past-month cannabis users reported daily use. We identified five themes: experiences of the relationship between cannabis use and mental health; cannabis use in response to barriers encountered with formal mental health supports; negative experiences with mental health services related to race, gender identity, and cannabis use; adverse effects of discrimination on mental health and cannabis use; and strategies to improve mental health programs. Interviews facilitated a deeper understanding of the complex relationship between cannabis use and mental health outcomes among racialized individuals. Clinical practice guidelines and training are recommended for healthcare providers to enhance culturally sensitive care regarding cannabis use and mental health. Research exploring risks and benefits of self-medication using cannabis would enrich our understanding of its implications specifically for this population.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".