Socio-economic factors associated with alcohol and cannabis use across waves of the COVID-19 pandemic: an intersectional analysis of a repeated cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: This study examined trends in cannabis and alcohol use among Canadian adults and across socio-economic subgroups over four waves of the COVID-19 pandemic from 2020 to 2022. Interactions between socio-economic status (SES) and gender, ethnoracial background, and age as they are associated with alcohol and cannabis use were examined. METHODS: Data were collected from nine consecutive web-based cross-sectional surveys of adults living in Canada (8,943 participants) conducted from May 2020 to January 2022. Substance use measurements included self-reported changes in alcohol and cannabis use compared to before the pandemic, heavy episodic drinking (HED) (i.e., consumption of 4 or more and 5 or more standard drinks on one drinking occasion for men and women, respectively), and cannabis use in the past 7 days. The Wilcoxon rank-sum test was used to test for equality of the prevalence of substance use. Stepwise logistic regression models were used to assess the associations of SES and its interactions with gender, ethnoracial background, and age with alcohol and cannabis use. RESULTS: The prevalence of increased alcohol and cannabis use differed through the pandemic waves depending on SES. The prevalence of HED and increased cannabis use were similar across SES groups. Having a moderate or high household income and being unemployed were associated with HED and a perceived increase in alcohol use. People in racial and ethnic minority groups with a household income of $40,000 to $79,999 had greater odds of engaging in HED than White persons in households with less than $40,000. Women and individuals aged 40 to 59 years with a high household income (≥$120,000) were more likely to report increased alcohol consumption than men and individuals aged 18 to 39 years in households with an income of less than $40,000. Protective factors associated with HED were being a woman with a university degree and an older adult with a college degree. Protective factors associated with cannabis use or perceived increases in cannabis use included women with a university degree, aged 39 years or more with a university or college degree and being in racial and ethnic minority groups with a university degree. CONCLUSIONS: Associations between SES and substance use differ by gender, race and age. To reduce health disparities, public health interventions should account for these interactions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".