The intersection of internalizing symptoms and alcohol use during the COVID-19 pandemic: A prospective cohort study
Bibliographic record
Abstract
Given the well-established relationship between alcohol and internalizing symptoms, potential increases in depression and anxiety during the COVID-19 pandemic may lead to increases in alcohol consumption and binge drinking. This study examines this association from before to during two phases of the pandemic in a cohort of Canadian youth. We used linked data from a sub-sample of 1901 secondary school students who participated in three consecutive school years of the Cannabis use, Obesity, Mental health, Physical activity, Alcohol use, Smoking, and Sedentary behaviour (COMPASS) study between 2018/19 and 2020/21. Separate multilevel logistic regression models examined the association between depression and anxiety symptoms with odds of escalation and reduction (vs. maintenance) and initiation (vs. abstinence) of alcohol consumption. Results show that depression and anxiety symptoms significantly increased over the three years, and these changes were moderated by changes in alcohol consumption and binge drinking. Students with increased depression symptoms were less likely to reduce their alcohol consumption in the early pandemic (Adjust odds ratio [AOR] 0.94, 95% CI:0.90-0.98), more likely to initiate alcohol consumption in the ongoing pandemic period (AOR 1.03, 95% CI: 1.01-1.05), and more likely to initiate binge drinking in both periods. The depression-alcohol use association was stronger among females than males. This study demonstrates a modest association between internalizing symptoms and alcohol use, particularly for depression symptoms and in females. The identified depression-alcohol use association suggests that preventing or treating depression might be beneficial for adolescent alcohol use and vice versa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".