Use of cannabis for mental health in the Canadian territories: a cross-sectional study
Bibliographic record
Abstract
Background Population prevalence and patterns of cannabis use for mental health (CUMH) are underexplored. This is important to understand in the Canadian territories which has the highest prevalence of cannabis use in Canada. This study aimed to examine socio-demographic factors associated with CUMH in the territories and associations between CUMH and cannabis use outcomes.Methods This study is a cross-sectional analysis of survey data from the 2022 Cannabis Policy Study in the Territories, including 2431 respondents aged 16+. Multivariable logistic regression models were used to examine socio-demographic characteristics associated with CUMH. Among past 12-month cannabis consumers, multivariable logistic regression models were used to examine whether CUMH was associated with daily/near-daily use, cannabis product type, healthcare interactions, and self-reported impacts on mental health, controlling for socio-demographic characteristics.Results Overall, 29.6% of all participants, and 55.5% of past 12-month cannabis consumers reported ever using cannabis for mental health. Use for mental health was higher among those with lower education, lower perceived income adequacy, and younger ages. Those reporting CUMH were more likely to report daily/near-daily use (ORadj = 3.00, 95%CI: 2.01–4.49), potent product types like solid concentrates (ORadj = 2.76, 1.62–4.70), and perceived positive impacts on mental health (ORadj = 3.71, 2.49–5.52).Conclusion Due to the high prevalence of CUMH, more research is needed to examine its long-term impacts and effectiveness. Future research is also needed to understand the social context underlying socioeconomic inequalities in CUMH, including access to mental healthcare and harm reduction measures for mitigating adverse mental health impacts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".