Changes in alcohol consumption in Spain between 1990 and 2019
Bibliographic record
Abstract
Spain is one of the countries of the European Union in which alcohol consumption has decreased in the past decades. The aim of this paper is to distinguish different phases of the level of alcohol consumption in Spain since 1990. Adult alcohol consumption per capita data between 1990 and 2019 were analysed for temporal trends using the Joinpoint regression model. An additional analysis using interrupted time-series between 1962 and 2016 was performed using data from Global Information System on Alcohol and Health. Data from the survey on alcohol and other drugs in Spain were collected and a narrative review was conducted to identify possible reasons for the trends found. Five point changes were identified on the timeline between 1990 and 2019, including: a decrease of 3.2% per year from 1990 to 1995, an increase of 1.1% per year from 1995 to 2000, a period of stability from 2000 to 2006, a decrease of 4.5% per year from 2006 to 2011, and a period of stability from 2011 onwards. These changes can largely be explained by the different public health measures carried out by the Spanish government, as well as the change in the pattern of consumption in society, which shifted its alcoholic beverage preference from wine to beer, and increased its binge-drinking behaviour. Further studies such as interrupted time-series analyses should test if indeed the hypothesized measures on public health have been effective; this could inform future policies in Spain and in other countries.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".