Alcohol Health Warning Labels: A Rapid Review with Action Recommendations
Bibliographic record
Abstract
A rapid review of research on health warning labels located on alcohol containers (AWLs) was conducted. Using five search engines (Embase, Medline, Pubmed, Scopus, Psyinfo), 2975 non-duplicate citations were identified between the inception date of the search engine and April 2021. Of those, 382 articles were examined and retrieved. We selected 122 research papers for analysis and narrative information extraction, focusing on population foci, study design, and main outcomes. Research included public opinion studies, surveys of post-AWL implementation, on-line and in-person experiments and real-world quasi-experiments. Many studies focused on the effects of the 1989 United States Alcoholic Beverage Labeling Act on perceptions, intentions and behavior. Others focused on Australia, Canada, the United Kingdom, England or Scotland, Italy and France. There was substantial variation in the design of the studies, ranging from small-scale focus groups to on-line surveys with large samples. Over time, evidence has been emerging on label design components, such as large size, combination of text and image, and specific health messaging, that is likely to have some desired impact on knowledge, awareness of risk and even the drinking behavior of those who see the AWLs. This body of evidence provides guidance to policy-makers, and national and regional authorities, and recommendations are offered for discussion and consideration.
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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".