Psychological and Demographic Determinants of Substance Use and Mental Health During the COVID-19 Pandemic
Bibliographic record
Abstract
Background:Alcohol consumption and distress have increased among Canadians since the start of the COVID-19 pandemic. Methods:We examined whether sociodemographic and COVID-19-related worries are associated with various combinations of alcohol consumption and comorbid psychological distress variables among a Canadian sample of adults. Data were derived from a sample of Canadian adults (N= 1,005, 49.6% female) who participated in an online survey in May 2020. Four multivariate ordinal logistic regression models were used to estimate the odds of binge drinking, increased alcohol consumption during the pandemic, and psychological distress. Predictor variables used in the analyses included self-reported sociodemographic characteristics, financial worries, COVID-19 impact on work, and worrying about getting ill. Results:Women were found to have higher odds of increased drinking and anxiety. Also being divorced, separated, or widowed was associated with higher odds of binge drinking and anxiety, and binge drinking and depression. Furthermore, being 60 or older was associated with lower odds of binge drinking and depression and increased drinking and depression, as well as lower odds of increased drinking and depression and increased drinking and anxiety. High income groups were associated with higher odds of binge drinking, increased drinking, and mental distress. Compared to those less worried, being very worried about finances were associated with higher odds of binge drinking and anxiety, increased drinking and anxiety, and increased drinking and depression. Also, being very worried about getting ill with COVID was associated with higher odds of binge drinking and anxiety and increased drinking and anxiety. Conclusion:Our findings identify several demographic and COVID-related worries for increased odds of alcohol intake and co-morbid psychological distress during the COVID-19 pandemic, including identifying as a woman, high income groups, being divorced, separated or widowed, and experiencing financial worries and COVID illness worries. These characteristics should be considered when developing prevention and treatment programs for adults with problematic alcohol use and comorbid anxiety and depression.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".