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Record W4400027731 · doi:10.1016/s2468-2667(24)00102-6

Effect of alcohol health warning labels on knowledge related to the ill effects of alcohol on cancer risk and their public perceptions in 14 European countries: an online survey experiment

2024· article· en· W4400027731 on OpenAlexaff
Daniela Correia, Daša Kokole, Jürgen Rehm, Alexander Tran, Carina Ferreira‐Borges, Gauden Galea, Tiina Likki, Aleksandra Olsen, Maria Neufeld

Bibliographic record

VenueThe Lancet Public Health · 2024
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsCentre for Addiction and Mental Health
FundersEuropean CommissionWorld Health Organization
KeywordsAlcoholHarmPerceptionEnvironmental healthHarm reductionPublic healthCancerMedicineRisk perceptionPsychologySocial psychologyNursing

Abstract

fetched live from OpenAlex

Background Alcohol health-warning labels are a policy option that can contribute to the reduction of alcohol-related harms, but their effects and public perception depend on their content and format. Our study aimed to investigate the effect of health warnings on knowledge that alcohol causes cancer, the perceptions of three different message topics (responsible drinking, general health harm of alcohol, and alcohol causing cancer), and the role of images included with the cancer message. Methods In this online survey experiment, distributed in 14 European countries and targeting adults of the legal alcohol-purchase age who consumed alcohol, participants were randomly allocated to one of six label conditions using a pseudorandom number generator stratified by survey language before completing a questionnaire with items measuring knowledge and label perceptions. Effect on knowledge was assessed as a primary outcome by comparing participants who had increased knowledge after exposure to labels with the rest of the sample, for the six label conditions. Label perceptions were compared between label conditions as secondary outcomes. Findings 19 110 participants completed the survey and were eligible for analysis. Our results showed that a third of the participants exposed to the cancer message increased their knowledge of alcohol causing cancer (increase for 1131 [32·5%, 95% CI 29·8 to 35·2] of 3409 participants [weighted percentage] for text-only message; increase for 1096 [33·3%, 30·4 to 36·2] of 3198 [weighted percentage] for message inlcuding pictogram; and increase for 1030 [32·5%, 29·6 to 35·4] of 3242 [weighted percentage] for message including graphic image), compared with an increase for 76 (2·4%, –1·2 to 6·0) of 3018 participants who viewed the control message. Logistic regression showed that cancer messages increased knowledge compared with the control label (odds ratio [OR] text only 20·20, 95% CI 15·88 to 26·12; OR pictogram 21·16, 16·62 to 27·38; OR graphic-image 20·61, 16·19 to 26·68). Cancer messages had the highest perceived impact and relevance, followed by general health harm and responsibility messages. Text-only and pictogram cancer messages were seen as clear, comprehensive, and acceptable, whereas those including an image of a patient with cancer had lower acceptability and the highest avoidance rating of all the labels. The only identified interaction between perceptions and experimental conditions (with gender) indicated higher comprehensibility and acceptability ratings of cancer labels than responsibility messages and control labels by women, with the results reversed in men. Interpretation Health warnings are an effective policy option to increase knowledge of alcohol causing cancer, with a generalisable effect across several countries. Europeans consider alcohol health-warning labels to be comprehensible and acceptable, with cancer-specific health warnings having the highest perceived impact and relevance. Funding EU4Health.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.114
GPT teacher head0.423
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2024
Admission routes1
Has abstractyes

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