A new perspective on European drinking cultures: a model‐based approach to determine variations in drinking practices among 19 European countries
Bibliographic record
Abstract
BACKGROUND AND AIMS: In recent decades, alcohol drinking in the European Union has been characterized by increasing homogenization of levels of drinking coupled with an overall decrease. This study examined whether we can still distinguish distinct practices of drinking by addressing two research questions: (1) are drinking practices still characterized by the choice of a certain alcoholic beverage; and (2) how do drinking practices vary across countries? DESIGN: Cross-sectional study: latent-class analyses of drinking variables and fractional response regression analyses of individual characteristics for individual-level class endorsement probabilities, respectively. SETTING: Nineteen European countries and one autonomous community. PARTICIPANTS: A total of 27 170 past-year drinkers aged 18-65 years in 2015. MEASUREMENTS: Data were collected through the Standardized European Alcohol Survey included frequency of past-year drinking, pure alcohol intake per drink day, occurrence of monthly risky single-occasion drinking and preferred beverage, together with socio-demographic data. FINDINGS: Three latent classes were identified: (1) light to moderate drinking without risky single-occasion drinking [prevalence: 68.0%, 95% confidence interval (CI) = 66.7-69.3], (2) infrequent heavy drinking (prevalence: 12.6%, 95% CI = 11.5-13.7) and (3) regular drinking with at least monthly risky single-occasion drinking (prevalence: 19.4%, 95% CI = 18.1-20.9). Drinking classes differed considerably in beverage preference, with women reporting a generally higher share of wine and men of beer drinking. Light to moderate drinking without risky single-occasion drinking was the predominant drinking practice in all locations except for Lithuania, where infrequent heavy drinking (class 2) was equally popular. Socio-demographic factors and individual alcohol harm experiences (rapid alcohol on-line screen) explained up to 20.5% of the variability in class endorsement. CONCLUSIONS: Beverage preference appears to remain a decisive indicator for distinguishing Europeans' drinking practices. In most European countries, multiple drinking practices appear to be present.
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.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".