A systematic review on the impact of alcohol warning labels
Bibliographic record
Abstract
reference lists (961 following duplicate removal). Article titles and abstracts were screened, leaving the full text of 96 for review. The full-text review identified 77 articles meeting inclusion/exclusion criteria which are included here. Risk of bias among included studies was examined using the Evidence Project risk of bias tool. Findings fell into five categories of alcohol use proxies including knowledge/awareness, perceptions, attention, recall/recognition, attitudes/beliefs, and intentions/behavior. Real-world studies highlighted an increase in AWL awareness, alcohol-related risk perceptions (limited findings), and AWL recall/recognition post-AWL implementation; these findings have decreased over time. Conversely, findings from experimental studies were mixed. AWL content/formatting and participant sociodemographic factors also appear to influence the effectiveness of AWLs. Findings suggest conclusions differ based on the study methodology used, favoring real-world versus experimental studies. Future research should consider AWL content/formatting and participant sociodemographic factors as moderators. AWLs appear to be a promising approach for supporting more informed alcohol consumption and should be considered as one component in a comprehensive alcohol control strategy.
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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 | 0.033 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".