The impact of lower strength alcohol products on alcohol purchases: ARIMA analyses based on 4 million purchases by 69 803 households, 2015–2019
Bibliographic record
Abstract
BACKGROUND: Lowering the strength of alcohol products could lead to less alcohol being bought and drunk. In its prevention White Paper, the UK Government aims to promote a significant increase in the availability of alcohol-free and low-alcohol products by 2025. METHODS: Through descriptive analysis and ARIMA modelling of >4 million alcohol purchases from 69 803 British households, we study the potential impact of lower strength alcohol products in reducing household purchases of grams of alcohol over 2015-2019. Households are divided into predominantly beer, wine or spirits purchasers. RESULTS: Over 5 years, there were decreases in purchases of grams of alcohol within beer amongst beer-purchasing households and increases in purchases of grams of alcohol within wine and spirits amongst, respectively, wine- and spirits-purchasing households. Almost all the changes were due to beer-purchasing households buying less regular strength beer, and wine and spirits-purchasing households buying, respectively, more regular strength wine and spirits, rather than increases in purchases of no- and low-alcohol products. CONCLUSIONS: In general, lower strength alcohol products have not contributed to British households buying fewer grams of alcohol over the 5-year follow-up period during 2015-2019.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".