Online alcohol sales and home delivery: An international policy review and systematic literature review
Bibliographic record
Abstract
Background: Online alcohol sales are experiencing rapid growth in many places, accelerated by the COVID-19 pandemic, prompting new laws and regulations. There are no comprehensive and systematic analyses of the laws or their effectiveness. Objective: To summarise international policies governing online alcohol sale and delivery, including changes occurring with COVID-19, and examine available evidence of retailer compliance with such policies. Method: A policy review of 77 jurisdictions in six English-speaking OECD countries: United States, Canada, United Kingdom, Ireland, Australia and New Zealand. We synthesised policies according to ten elements identified as potentially relevant for public health regulation. A systematic literature review of compliance evaluations in Medline, Medline Epub, EMBASE, CINAHL, Web of Science and Google Scholar. Results: 72 of 77 jurisdictions permitted online alcohol sales and home delivery. Few jurisdictions require age verification at the time of purchase (n = 7), but most require it at delivery (n = 71). Since the COVID-19 pandemic began, most jurisdictions (69%) have either temporarily or permanently relaxed liquor regulations for alcohol home delivery. Three articles examined retailer compliance with age restrictions and found relatively low compliance (0%-46%). Conclusion: Many jurisdictions permit the online sale and delivery of alcohol, but regulation of these sales varies widely. In most, regulations do not meet the same standard as bricks-and-mortar establishments and may be insufficient to prevent youth access.
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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 | 0.017 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".