Alcohol use and COVID-19: three-year impact for the Czech Republic
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to describe alcohol consumption and its changes in the Czech Republic during the period of governmental restrictions related to the COVID-19 pandemic. To this goal, an overview of the governmental measures that may have affected alcohol consumption was compiled from various sources (mostly media reports). METHODS: The paper analyses three surveys where the group from Charles University, Prague, participated in design and execution: the European Study Group on Alcohol use and COVID-19 (ESAC) convenience online survey that (in Czechia) took place at the onset of the COVID-19 pandemic in Europe, in April through June 2020 (n = 1,434), a computer-assisted web interviews (CAWI) survey carried out in November 2021 (n = 790), and another CAWI survey carried out in November 2022 (n = 1,738). These are complemented by a brief overview of official data on alcohol sales between 2019 and 2021 in Czechia from two sources, Ministry of Finance of the Czech Republic and the Nielsen IQ market research company. RESULTS: Changes in alcohol use appeared to be distributed among the population unequally, when the pandemic influenced different population groups in a different way. In all three surveys, the majority of respondents reported no change in their drinking frequency or quantity. Decreases in alcohol use were more prevalent than increases. Most pronounced changes were detected in at-risk drinkers, who have increased their alcohol use. Official revenue data suggest a small reduction in total alcohol sales in 2019-2021. CONCLUSIONS: Our research results do not support the hypothesis that substantial and unexpected social and economic changes caused by the COVID-19 pandemic, along with corresponding measures, acted as stressors that would have caused the majority of people in Czechia changing their behaviour related to alcohol use; however, high-risk users increased their consumption.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".