Cannabis Use During the COVID-19 Pandemic in Canada: A Repeated Cross-sectional Study
Bibliographic record
Abstract
OBJECTIVES: In the context of the ongoing coronavirus disease pandemic in Canada, we aimed to (1) characterize trends in cannabis use in the overall population; and (2) characterize patterns of and identify risk characteristics associated with an increase in cannabis use among those who used cannabis. METHODS: Data were obtained from three waves of an online, repeated cross-sectional survey of adults residing in Canada (May 08-June 23, 2020; N = 3012). Trends were assessed using Cochran-Armitage and chi-square tests, and risk characteristics were identified using logistic regression analyses. RESULTS: Cannabis use in the overall population remained stable during the months of May and June. Among those who used cannabis, about half increased their cannabis use compared to before the start of the pandemic. This proportion of an increase in cannabis use among those who used cannabis remained consistent across the survey waves. Risk characteristics associated with higher odds of an increase in cannabis use included residence in the central region (Odds ratio, 95% confidence intervals: 1.93, 1.03-3.62), being 18 to 29 years old (2.61, 1.32-5.17) or 30 to 49 years old (1.85, 1.07-3.19), having less than college or university education (1.86, 1.13-3.06) and being somewhat worried about the pandemic's impact on personal finances (1.73, 1.00-3.00). CONCLUSIONS: A large proportion of those who used cannabis have increased cannabis use during the pandemic, suggesting a need for interventions to limit increased cannabis use, policy measures to address cannabis-attributable harms, and continued monitoring of cannabis use during and after the pandemic.
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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) | 0.001 | 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".