Applying a gendered lens to understanding self-reported changes in alcohol and cannabis consumption during the second wave of the COVID-19 pandemic in Canada, September to December 2020
Bibliographic record
Abstract
INTRODUCTION: Increased alcohol and cannabis consumption and related harms have been reported since the beginning of the COVID-19 pandemic. Existing evidence shows that substance use and related harms differ by gender. Yet, no Canadian study has applied a gendered lens to alcohol and cannabis consumption use during this time. Our objectives were to (1) provide gender-specific prevalence estimates of self-reported increased alcohol and cannabis use; and (2) examine gender-specific associations between sociodemographic and mental health variables and alcohol and cannabis use. METHODS: Using data from the Survey on COVID-19 and Mental Health, we calculated nationally representative, gender-specific prevalence estimates and disaggregated them by sociodemographic and mental health variables. Four logistic regression models were used to assess the likelihood of self-reported increased alcohol and cannabis use. RESULTS: The prevalence of self-reported increase in alcohol use (16.2% women; 15.2% men) and cannabis use (4.9% women; 5.8% men) did not differ by gender. For both genders, income, racialized group membership, working in the past week, being a parent/legal guardian of a child aged under 18 and screening positive for depression and anxiety were associated with increased alcohol use. Men and women who were between the ages of 18 to 44, screened positive for depression, or both, were more likely to report increased cannabis use. For women, education was significantly associated with increased alcohol use. For men, being a parent/legal guardian was significantly associated with lower odds of increased cannabis use. CONCLUSION: Sociodemographic factors, as well as depression and anxiety, were similarly associated with increased alcohol and cannabis use for both men and women in the second wave of 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".