Evaluation of Alcohol Industry Action to Reduce the Harmful Use of Alcohol: Case Study from Great Britain
Bibliographic record
Abstract
AIMS: To describe a case study in the British market of one of the global beer-producing companies that has set a target to increase the proportion of its products with an alcohol by volume (ABV) of 3.5% or less, and to reduce the mean ABV of its beer products. METHODS: Descriptive statistics and time-series analyses using Kantar Worldpanel's British household purchase data for 2015-2018. RESULTS: As assessed by British household purchase data, 15.7% of the company's beer products had an ABV of 3.5% or less in 2018, compared with 8.8% in 2015. The mean ABV of its beer products dropped from 4.69 in 2015 to 4.55 in 2018. Associated with these changes, the increase in purchased grams of alcohol in all beer that occurred during 2015-2016 (standardized coefficient = 0.007), plateaued during 2017 (standardized coefficient = -0.006) and decreased during 2018 (standardized coefficient = -0.034). Similar findings applied to the purchased grams of alcohol in beer other than ABI beer, suggesting some switching from other beer products to ABI products; and in all alcohol, suggesting, on balance, no overall switching to higher strength products. Greater decreases in purchases were found in the younger age groups, the highest purchasing households in terms of grams of alcohol, class groups D and E, and Scotland; there was no clear pattern by household income. CONCLUSIONS: The proportion of the company's beer purchased in Great Britain that had an ABV of 3.5% or less increased since the launch of the target, and the mean ABV of its beer products decreased. The changes were associated with reduced purchases of grams of alcohol within its beer products. The associated reductions in purchases of alcohol in all beer and in all alcohol products suggest no evidence of overall switching to other higher strength beer or alcohol products. Other beer-producing companies should undertake similar initiatives. A regulatory tax environment should be introduced to ensure a level-playing field favouring lower alcohol concentration across all beer and other alcohol products.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".