Reducing alcohol harms whilst minimising impact on hospitality businesses: ‘Sweetspot’ policy options
Bibliographic record
Abstract
BACKGROUND: During COVID-19, hospitality businesses (e.g. bars, restaurants) were closed/restricted whilst off-sales of alcohol increased, with health consequences. Post-covid, governments face lobbying to support such businesses, but many health services remain under pressure. We appraised 'sweetspot' policy options: those with potential to benefit public services and health, whilst avoiding or minimising negative impact on the hospitality sector. METHODS: We conducted rapid non-systematic evidence reviews using index papers, citation searches and team knowledge to summarise the literature relating to four possible 'sweetspot' policy areas: pricing interventions (9 systematic reviews (SR); 14 papers/reports); regulation of online sales (1 SR; 1 paper); place-shaping (2 SRs; 18 papers/reports); and violence reduction initiatives (9 SRs; 24 papers/reports); and led two expert workshops (n = 11). RESULTS: Interventions that raise the price of cheaper shop-bought alcohol appear promising as 'sweetspot' policies; any impact on hospitality is likely small and potentially positive. Restrictions on online sales such as speed or timing of delivery may reduce harm and diversion of consumption from on-trade to home settings. Place-shaping is not well-supported by evidence and experts were sceptical. Reduced late-night trading hours likely reduce violence; evidence of impact on hospitality is scant. Other violence reduction initiatives may modestly reduce harms whilst supporting hospitality, but require resources to deliver multiple measures simultaneously in partnership. CONCLUSIONS: Available evidence and expert views point to regulation of pricing and online sales as having greatest potential as 'sweetspot' alcohol policies, reducing alcohol harm whilst minimising negative impact on hospitality businesses.
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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 | 0.043 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".