Changes in cannabis attitudes and perceptions in the five years following recreational legalization in Canada: Findings from an observational cohort study of community adults
Bibliographic record
Abstract
BACKGROUND: Social acceptability and perceived risks/benefits are key attitudinal factors that influence substance use, and a major concern about cannabis legalization is an increase in more favourable attitudes ultimately leading to greater cannabis misuse. This study investigated perceptions of cannabis acceptability and risks/benefits over the 5 years following legalization in Canada, the first G7 nation to have legalized cannabis nationally, in a longitudinal observational cohort of community adults. METHODS: Participants (60 % female, median age = 29, 48% reporting cannabis use pre-legalization) were non-clinical adults from the general community who were assessed up to 11 times from September 2018 to October 2023 (mean waves = 9.9). Overall temporal attitudinal changes and whether changes were moderated by pre-legalization cannabis use status were examined. RESULTS: Significant increases over time were present for social acceptability of any recreational cannabis use (OR [95% CI]: 1.06 [1.05, 1.07]) and trying cannabis (1.02 [1.01, 1.03]), while acceptability of medical cannabis use decreased (0.95 [0.94, 0.96]). Meanwhile, regular cannabis use was perceived as riskier (0.97 [0.96, 0.98]) and addiction potential was perceived as greater (0.94 [0.93, 0.95]) over time. Perceived health-related benefits of cannabis were significantly less likely to be endorsed over time, while there were significant increases in perceived risks, including exacerbating stress, anxiety, and depression; exacerbating existing medical conditions; and disrupting sleep. Moderator analyses found participants not using cannabis pre-legalization showed significantly steeper increases towards greater social acceptability of occasional and regular use, and less steep increases in endorsement of cannabis-related risks. CONCLUSIONS: Shifting social acceptability of cannabis post-legalization in Canada is paralleled by increases in perceived health-related risks and decreases in perceived benefits. Continued surveillance of attitudinal changes following legalization in Canada is warranted to inform the impacts in the largest national legal cannabis jurisdiction as well as other jurisdictions considering regulatory reform.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".