Changes in Alcohol Use in Denmark during the Initial Months of the COVID-19 Pandemic: Further Evidence of Polarization of Drinking Responses
Bibliographic record
Abstract
INTRODUCTION: The year 2020 was marked by the COVID-19 pandemic. Policy responses to COVID-19 affected social and economic life and the availability of alcohol. Previous research has shown an overall small decrease in alcohol use in Denmark in the first months of the pandemic. The present paper focused on identifying which subgroups of individuals had decreased or increased their consumption. MATERIALS AND METHODS: Data were collected between May and July 2020 (n = 2,566 respondents, convenience sample). Weights were applied to reflect the actual Danish general population. Variables included the pre-pandemic alcohol consumption, change in alcohol consumption in the past month, socio-demographics, and reported economic consequences. Responses to a single item assessing changes in alcohol consumption in the past month were classified as no change, increase, or decrease in consumption. Regression models investigated how changes in consumption were linked to pre-pandemic drinking levels, socio-demographics (gender, age groups, education), and reported economic consequences. RESULTS: While 39% of participants reported decreased consumption levels and 34% had stable levels, 27% increased consumption. Characteristics associated with changes in consumption were associated with both increases and decreases in consumption: younger people, those with higher consumption levels before the pandemic, and those with lower education more often both reported increases as well as decreases in consumption. DISCUSSION/CONCLUSIONS: We confirmed that more people decreased rather than increased their alcohol consumption in the first few months of the pandemic in Denmark. Characteristics associated with changes in consumption such as younger age, higher consumption levels, and lower education demonstrated a polarization of drinking since these were associated with both increases and decreases in consumption. Public health authorities should monitor alcohol use and other health behaviours for increased risks during 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".