Alcohol consumption during the COVID‐19 pandemic in Europe: a large‐scale cross‐sectional study in 21 countries
Bibliographic record
Abstract
AIMS: To investigate changes in alcohol consumption during the first months of the COVID-19 pandemic in Europe as well as its associations with income and experiences of distress related to the pandemic. DESIGN: Cross-sectional on-line survey conducted between 24 April and 22 July 2020. SETTING: Twenty-one European countries. PARTICIPANTS: A total of 31 964 adults reporting past-year drinking. MEASUREMENTS: Changes in alcohol consumption were measured by asking respondents about changes over the previous month in their drinking frequency, the quantity they consumed and incidence of heavy episodic drinking events. Individual indicators were combined into an aggregated consumption-change score and scaled to a possible range of -1 to +1. Using this score as the outcome, multi-level linear regressions tested changes in overall drinking, taking into account sampling weights and baseline alcohol consumption [Alcohol Use Disorder Identification Test (AUDIT-C)] and country of residence serving as random intercept. Similar models were conducted for each single consumption-change indicator. FINDINGS: The aggregated consumption-change score indicated an average decrease in alcohol consumption of -0.14 [95% confidence interval (CI) = -0.18, -0.10]. Statistically significant decreases in consumption were found in all countries, except Ireland (-0.08, 95% CI = -0.17, 0.01) and the United Kingdom (+0.10, 95% CI = 0.03, 0.17). Decreases in drinking were mainly driven by a reduced frequency of heavy episodic drinking events (-0.17, 95% CI = -0.20, -0.14). Declines in consumption were less marked among those with low- or average incomes and those experiencing distress. CONCLUSIONS: On average, alcohol consumption appears to have declined during the first months of the COVID-19 pandemic in Europe. Both reduced availability of alcohol and increased distress may have affected consumption, although the former seems to have had a greater impact in terms of immediate effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".