Reduction of Alcoholic Strength: Does It Matter for Public Health?
Bibliographic record
Abstract
In this work, reduction of alcoholic strength was discussed as a means to reduce consumption and alcohol-attributable harm. Statistical modelling was conducted to (1) estimate its potential for the largest six Western and Central European countries (France, Germany, Italy, Poland, Spain, UK); (2) calculate the increase in taxation necessary to reach this potential, and (3) estimate the mortality gains achieved with the introduction of no- or low-alcohol beverages in the UK and Spain. The high public health potential of reducing alcoholic strength was demonstrated via modelling a scenario in which the strength of all beverages was reduced by 10%, which would avert thousands of deaths in these six European countries per year. However, methods by which to achieve these gains were not clear, as the alcohol industry has shown no inclination toward reductions in the alcoholic strength of beer, wine, or spirits via a reformulation on a large scale. The increase of excise taxation to achieve the public health gains of such a reduction would result in markedly increasing prices-a situation unlikely to be implemented in Europe. Finally, the introduction of beer and wine with an alcoholic strength below 0.5% led to some substitutions of higher-strength beverages, but did not show a marked public health impact. New taxation initiatives to achieve the potential of a reduction of alcoholic strength will need to be implemented.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".