COVID-19 Stressors and Cannabis and Alcohol Use in the Canadian Territories
Bibliographic record
Abstract
Background: The COVID-19 pandemic and related public health restrictions have been associated with high cannabis and alcohol use among Canadians. Little is known about cannabis and alcohol use in the Canadian territories during the pandemic, despite generally higher substance use rates. This study’s objective was to examine the association between self-reported changes in mental health status and financial stressors resulting from the pandemic on daily/almost daily cannabis use and heavy drinking in the Canadian territories. Methods: This study analyzed data from 993 individuals aged 16 and over residing in the territories collected from September to December 2021 as part of the 2021 Cannabis Policy Study in the Territories. Weighted logistic regression models estimated the association of self-reported mental health status (“worse”/“not worse”) and financial impact (“negative impact”/“no negative impact”) related to the pandemic with daily/almost daily cannabis use (≥5 days per week) and heavy drinking (≥ 4 drinks for women and ≥ 5 drinks for men per occasion at least once a month) in the past 12 months. Models were adjusted for sex, age group, education, perceived income adequacy, territory, ethnicity, living in a capital city, living alone, and having children ≤17 years old. Results: In adjusted models, self-reported worse mental health related to the pandemic had no significant associations with daily/almost daily cannabis use (OR = 1.17, 95% CI:0.72,1.91) and no association with heavy drinking (OR = 1.01, 95% CI:0.63,1.62). Self-reported negative financial impact showed associations above one for daily/almost daily cannabis use (OR = 1.10, 95% CI:0.58,2.08) and with heavy drinking (OR = 1.27, 95% CI:0.75,2.17), though confidence intervals crossed one, indicating no statistical significance. Conclusion: No significant associations were observed between COVID-19 stressors and higher substance use in late 2021. Continued monitoring of the long-term impacts of the pandemic on substance use in the Canadian territories is warranted.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".