Characteristics of Canadians likely to try or increase cannabis use following legalization for nonmedical purposes: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The Government of Canada legalized nonmedical use of cannabis in October 2018. Our objectives were to determine the percentage of Canadians intending to try or increase their cannabis use following legalization and to explore characteristics associated with this intent. METHODS: We used data from the 2018 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 39 000 households selected for recruitment for the survey, 17 089 respondents provided complete data (43.8%) and our weighted analysis represented 27 808 081 Canadians aged 15 years and older. An estimated 18.5% of respondents (95% confidence interval [CI] 17.6%-19.5%) indicated they intended to try or increase cannabis use following legalization. Being more likely to try or increase cannabis use was associated with younger age (15-24 yr v. ≥ 65 yr; adjusted OR 3.8, 95% CI 2.6-5.6; adjusted RI 20.1%, 95% CI 13.9%-26.2%), cannabis use in the past 3 months versus no use (adjusted OR 3.3, 95% CI 2.8-3.9; adjusted RI 20.4%, 95% CI 17.1%-23.6%), higher income (≥ $80 000 v. < $40 000; adjusted OR 1.5, 95% CI 1.3-1.9; adjusted RI 6.1%, 95% CI 3.2%-9.0%) and poor or fair mental health versus good to excellent mental health (adjusted OR 2.0, 95% CI 1.6-2.6; adjusted RI 11.5%, 95% CI 6.7%-16.2%). INTERPRETATION: Nearly 1 in 5 respondents reported that they intended to try or increase cannabis use after legalization; however, intention may not translate into behaviour. Continued monitoring should help to establish rates and patterns of cannabis use among Canadians following 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.003 | 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".