Factors Associated with Initiating Cannabis Use After Legalization in Canada: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Cannabis use has increased since the Government of Canada legalized nonmedical use in October 2018. We investigated demographic factors associated with initiating cannabis use after legalization. Materials and Methods: We used data from the 2018 and 2019 National Cannabis Survey and constructed multivariable regression models. Respondents' data were weighted and bootstrapped. We report relative measures of association as adjusted odds ratios (ORs) and absolute measures of association as adjusted risk increases (RIs). Results: Among the 58,195 households surveyed, 28,566 provided complete data (49%) and our weighted analysis represented 27,904,258 Canadians aged ≥ 15 years. Approximately one in five Canadians endorsed use of cannabis (19.8%), predominantly for nonmedical (9.5%) or combined medical and nonmedical (5.8%) reasons. Those who initiated cannabis use in the past 3 months (1.9%) were more likely to be younger (25–34 years vs. ≥ 65 years; adjusted OR 1.7, 95% confidence interval [CI] 1.1–2.8; adjusted RI 1.1%, 95% CI 0.1–2.0%), endorse poor to fair versus good to excellent physical health (adjusted OR 2.0, 95% CI 1.3–3.1; adjusted RI 1.7%, 95% CI 0.3–3.1%), and reside outside of Quebec (adjusted OR 1.4, 95% CI 1.1–2.0; adjusted RI 0.1%, 95% CI 0.6–1.1%). The 1% of Canadians who endorsed initiating use of cannabis due to legalization were more likely to reside outside of Quebec (adjusted OR 1.9, 95% CI 1.1–3.2; adjusted RI 0.5%, 95% CI 0.2–0.9%). Conclusion: Canadians initiating cannabis use after nonmedical legalization were likely to be younger and endorse worse physical health, and half of those using cannabis reported therapeutic use. Stricter policies, lower social acceptance, and less availability of cannabis in Quebec appear to have curtailed initiation of use after legalization.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".