Trends and correlates of cannabis use in Canada: a repeated cross-sectional analysis of national surveys from 2004 to 2017
Bibliographic record
Abstract
BACKGROUND: Cannabis is the most widely used drug in Canada. We examined the trends in past-year cannabis consumption by sociodemographic and geographic characteristics. METHODS: We conducted a repeated cross-sectional analysis of the Canadian Tobacco Use Monitoring Survey, the Canadian Tobacco, Alcohol and Drugs Survey and the Canadian Alcohol and Drug Use Monitoring Survey from 2004 to 2017. Respondents were aged 15 years and older. Past-year cannabis use was analyzed using multivariable logistic regression and segmented logistic regression. RESULTS: We analyzed 289 823 respondents (51% female) between 2004 and 2017. Between 2004 and 2017, the overall prevalence of cannabis use increased from 12.2% (95% confidence interval [CI] 11.0%-13.5%) to 18.7% (95% CI 16.2%-21.5%) among men and from 6.6% (95% CI 5.9%-7.4%) to 11.1% (95% CI 9.4%-13.0%) among women. The crude rate of change was greater between 2011 and 2017 than that between 2004 and 2011 in men (odds ratio [OR] per annual change: 1.08, 95% CI 1.05-1.11) and women (OR 1.11, 95% CI 1.07-1.15). After adjustment for age, education, tobacco smoking and province, the 2011-2017 trend was stronger in men (adjusted OR 1.24, 95% CI 1.05-1.46), but not in women (adjusted OR 1.13, 95% CI 0.93-1.37). Cannabis use was associated with tobacco smoking (OR 4.94, 95% CI 4.65-5.25). Heterogeneity was found in cannabis use trends by age, education and province. Cannabis use decreased among respondents aged 15-19 years and increased in other age groups. INTERPRETATION: Cannabis consumption in Canada has increased and varies by sex, age, level of education and geography. Increases vary by sociodemographic factors and may be faster among certain groups. Further studies are warranted post-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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".