Here to stay? Policy changes in alcohol home delivery and “to‐go” sales during and after <scp>COVID</scp>‐19 in the United States
Bibliographic record
Abstract
During the early phase of the COVID-19 pandemic, legislative changes that expanded alcohol home delivery and options for "to-go" alcohol sales were introduced across the United States to provide economic relief to establishments and retailers. Using data from the Alcohol Policy Information System, we examined whether these changes have persisted beyond the peak phase of the COVID-19 emergency and explored the implications for public health. Illustration of state-level policy data reveals that the liberalisation of alcohol delivery and "to-go" alcohol sales has continued throughout a 2-year period (2020 and 2021), with indications that many of these changes have or will become permanent after the pandemic. This raises concerns about inadequate regulation, particularly in preventing underage access to alcohol, and ensuing changes in drinking practices. In this commentary, we highlight the need for rigorous empirical evaluation of the public health impact of this changing policy landscape and underscore the potential risks associated with increased alcohol availability, including a corresponding increase in alcohol-attributable mortality and other alcohol-related harm, such as domestic violence. Policy makers should carefully consider public health consequences, whose costs may surpass short-term economic interests in the long term.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| 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".