Unrecorded Alcohol Consumption in Seven European Union Countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Unrecorded alcohol, that is, alcohol not reflected in official statistics of the country where it is consumed, contributes markedly to overall consumption of alcohol. However, empirical data on unrecorded alcohol consumption are scarce, especially in high-income countries. This study measures the contribution of unrecorded alcohol in 7 member states of the European Union. METHODS: Two categories of unrecorded consumption were assessed in general population surveys (reducing alcohol related harm Standardized European Alcohol Survey; n = 11,224): home-made alcohol and cross-border shopping. Country-specific logistic regressions were used to link respondent characteristics to odds of acquisition of unrecorded alcohol. Total per capita alcohol consumption was estimated under different assumptions of calculating unrecorded alcohol consumption. RESULTS: Individuals with higher drinking levels were more likely to acquire unrecorded alcohol in all 7 countries. In some countries, male sex and more affluent social class were also positively linked to acquisition of unrecorded alcohol. There was a substantial contribution of unrecorded alcohol to overall consumption in 5 out of 7 member states (Croatia, Finland, Greece, Hungary, Portugal), but not in Poland or Spain. In Greece, up to two-thirds of all alcohol consumed was estimated to be unrecorded. CONCLUSION: Unrecorded alcohol contributes to overall consumption even in high-income countries, and thus needs to be monitored. In monitoring, as many categories of unrecorded alcohol as possible should be clearly defined (e.g., surrogate alcohol) and included in future surveys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.008 |
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 it