Are Lower-Strength Beers Gateways to Higher-Strength Beers? Time Series Analyses of Household Purchases from 64,280 British Households, 2015–2018
Bibliographic record
Abstract
AIMS: Buying and consuming no- (per cent alcohol by volume, ABV = 0.0%) and low- (ABV = >0.0% and ≤ 3.5%) alcohol beers could reduce alcohol consumption but only if they replace buying and drinking higher-strength beers. We assess whether buying new no- and low-alcohol beers increases or decreases British household purchases of same-branded higher strength beers. METHODS: Generalized linear models and interrupted time series analyses, using purchase data of 64,280 British households from Kantar Worldpanel's household shopping panel, 2015-2018. We investigate the extent to which the launch of six new no- and low-alcohol beers affected the likelihood and volume of purchases of same-branded higher-strength beers. RESULTS: Households that had never previously bought a same-branded higher-strength beer but bought a new same-branded no- or low-alcohol beer were less than one-third as likely to go on and newly buy the same-branded higher-strength product. When they did later buy the higher-strength product, they bought half as much volume as households that had not bought a new same-branded no- or low-alcohol beer. For households that had previously purchased a higher-strength beer, the introduction of the new same-branded no- or low-alcohol beer was associated with decreased purchases of the volume of the higher-strength beer by, on average, one-fifth. CONCLUSIONS: The increased availability of new no- and low-alcohol beers does not seem to be a gateway to purchasing same-branded higher-strength beers but rather seems to replace purchases of these higher-strength products. Thus, introduction of new no- and low-alcohol beers could contribute to reducing alcohol consumption.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".