The Relationship Between Rates of Cannabis Use and Covid-19 Infection Rates During the Pandemic: An Analysis of Canada’s National Cannabis Survey
Bibliographic record
Abstract
Background: The well-documented relationship between mental health and substance use is corroborated by recent research on the impacts of the Covid-19 pandemic on cannabis use behavior. Social isolation, anxiety, depression, stress, and boredom are all linked to the greater prevalence of cannabis and other substance use. Objectives: To better understand the relationship between infection rates in Canada and cannabis use behavior, this research examines the prevalence and frequency of cannabis use across health regions in all 10 provinces at the height of the pandemic. Methods: Our analyses linked data from the National Cannabis Survey with Covid-19 case rates and cannabis availability through legal retail outlets at the end of 2020, 2 years after cannabis legalization came into effect. Hierarchical generalized linear models were employed, controlling for age, gender, SES, mental health, the number of cannabis stores per square kilometer, and prevalence of cannabis use in each health region prior to the pandemic. Results: Even after controlling for other predictors, our models show that those residing where infection rates are higher are more likely to use cannabis and use it more often. Conclusions: The findings of this study support investing in better-targeted harm reduction measures in areas hit hardest by the pandemic to address contributing societal conditions. The implications are noteworthy for drug policy observers in North America and other global jurisdictions pursuing evidence-based public health approaches to regulating cannabis and other substance use.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".