Socio-economic factors associated with substance use across different waves of the COVID-19 pandemic: an intersectional analysis of a repeated cross-sectional survey
Bibliographic record
Abstract
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, ethnicity/race, and age as they are associated with alcohol and cannabis use were examined. Methods Data were obtained from nine successive web-based cross-sectional surveys of adults living in Canada (8,943 participants) performed 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 5 standard drinks on one drinking occasion in the past 7 days 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. Logistic regression models were used to assess the associations of SES and its interactions with gender, ethnicity/race, 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 was associated with HED and increased alcohol use. Women with a high household income ($120,000+) and a high household income and aged 40–59 years were associated with increased alcohol use compared to men. Non-White individuals who reported other impacts of COVID-19 on their work had lower odds of reporting increased alcohol use. Protective factors associated with cannabis use included having a university degree, being a senior with a moderate/high household income, being aged 40–59 years with a university degree, being a senior with at least a post-secondary level education, and being a highly educated non-White individual. 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".