Factors Associated with Self-Reported Changes in Alcohol Use among Young Adults during the COVID-19 Pandemic: A Comparative Analysis between Canada and France
Bibliographic record
Abstract
While the COVID-19 pandemic impacted young adults’ alcohol use patterns, little is known about how changes in alcohol use may differ across different settings. Our objective was to identify and compare factors associated with changes in alcohol use among young adults in Canada and France during the first year of the COVID-19 pandemic. We conducted an online cross-sectional survey in October–December 2020 with young adults aged 18–29 (n = 5185) in Canada and France. In each country, weighted multinomial logistic regressions were performed to identify factors associated with self-reported decrease and increase in alcohol use separately (reference: no change). Respectively, 33.4% and 21.4% reported an increase in alcohol use in Canada and France, while 22.9% and 33.5% reported a decrease. Being 25–29 was a predictor of decrease in Canada, while living away from family was associated with an increase in France. In both countries, participants were more likely to report an increase if they reported depressive symptoms, smoking tobacco, or cannabis use. Conversely, those who had been tested for COVID-19 and those who were highly compliant with COVID-19 preventive measures were more likely to report a decrease. Efforts are needed to develop alcohol use interventions for young adults, including in ways that prioritize those with mental health challenges.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".