The effects of alcohol container labels on consumption behaviour, knowledge, and support for labelling: a systematic review
Bibliographic record
Abstract
Alcohol container labels might reduce population-level alcohol-related harms, but investigations of their effectiveness have varied in approach and quality. A systematic synthesis is needed to adjust for these differences and to yield evidence to inform policy. Our objectives were to establish the effects of alcohol container labels bearing one or more health warnings, standard drink information, or low-risk drinking guidance on alcohol consumption behaviour, knowledge of label message, and support for labels. We completed a systematic review according to Cochrane and synthesis without meta-analysis standards. We included all peer-reviewed studies and grey literature published from Jan 1, 1989, to March 6, 2024, in English, French, German, or Spanish that investigated the effects of alcohol container labels compared with no-label or existing label control groups in any population on alcohol consumption behaviour, knowledge of label message, or support for labels. Data were synthesised narratively as impact statements and assessed for risk of bias and certainty in the evidence. A protocol was preregistered (PROSPERO CRD42020168240). We identified 40 publications that studied 31 labels and generated 17 impact statements. 24 (60%) of 40 publications focused on consumption behaviour and we had low or very low certainty in 13 (59%) of 22 outcomes. Alcohol container labels bearing health warnings might slow the rate of alcohol consumption (low certainty), reduce alcoholic beverage selection (moderate certainty), reduce consumption during pregnancy (low certainty), and reduce consumption before driving (moderate certainty). Interventions with multiple types of rotating alcohol container labels likely substantially decrease alcohol use (moderate certainty) and reduce alcohol sales (high certainty). To the best of our knowledge, this is the first systematic review on multiple types of alcohol container labels assessing their effects for certainty in the evidence. Limitations included heterogeneity in label designs and outcome measurements. Alcohol container labels probably influence some alcohol consumption behaviour, with multiple rotating messages being particularly effective, although effects might vary depending on individual health literacy or drinking behaviour. Alcohol container labels might therefore be effective components of policies designed to address population-level alcohol-related harms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".