Changes in Cannabis Use Patterns in Psychiatric Populations Pre- and Post-Legalization of Recreational Cannabis Use in Canada: A Repeated Cross-Sectional Survey
Bibliographic record
Abstract
Objective: Since the federal Canadian government legalized cannabis in 2018, cannabis use in the general population has slightly increased. However, little is known about the impact of cannabis legalization on pattens of cannabis use in psychiatric populations. Method: We studied changes in daily/almost daily and average 30-day cannabis use amongst individuals currently using cannabis who reported past 12-month experiences of specific mental health disorders and among those without past 12-month experiences of any mental health disorder before and after Canadian legalization of recreational cannabis use (N = 13,527). Data came from Canadian respondents in Wave 1 (August–October 2018), Wave 2 (September–October 2019), and Wave 3 (September–November 2020) of the International Cannabis Policy Study (ICPS). Results: After adjustment for covariates, among individuals currently using cannabis, the odds of using cannabis daily/almost daily increased only in individuals with schizophrenia between Wave 1 and Waves 3 (aOR = 9.19, 95% CI: 2.46 – 34.37). Similarly, significant increases in average 30-day cannabis use between Wave 1 (M = 12.80, SE = 1.65) and Wave 3 (M = 18.07, SE = 1.03) were observed only among individuals with schizophrenia [F (1,2) = 4.58, p < .05). No significant changes in daily/almost daily or average past 30-day cannabis use were observed in those without mental health problems or those reporting anxiety, depression, PTSD, bipolar disorder, or substance use disorders. Conclusions: Since legalization, cannabis use has significantly increased only among people with schizophrenia, highlighting the need for targeted public health prevention programs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".