Impact of front-of-pack labels on the perceived healthfulness of a sweetened fruit drink: A randomised experiment in five countries
Bibliographic record
Abstract
AbstractObjective:Front-of-pack (FOP) nutrition labelling is a globally recommended strategy to encourage healthier food choices. We evaluated the effect of FOP labels on the perceived healthfulness of a sweetened fruit drink in an international sample of adult consumers.Design:Six-arm randomised controlled experiment to examine the impact of FOP labels (no label control, Guideline Daily Amounts (GDA), Multiple Traffic Lights, the Health Star Ratings (HSR), Health Warning Labels, and ‘High-in’ Warning Labels (HIWL)) on the perceived healthfulness of the drink. Linear regression models by country examined healthfulness perceptions on FOP nutrition labels, testing for interactions by demographic characteristics.Setting:Online survey in 2018 among participants from Australia, Canada, Mexico, United Kingdom (UK) and United States.Participants:Adults (≥18 years, n 22 140).Results:Compared with control, HIWL had the greatest impact in lowering perceived healthfulness (β from −0·62 to −1·71) across all countries. The HIWL and the HSR had a similar effect in Australia. Other labels were effective in decreasing the perceived healthfulness of the drink within some countries only, but to a lower extent. The GDA did not reduce perceived healthfulness in most countries. In the UK, the effect of HIWL differed by age group, with greater impact among older participants (> 40 years). There were no other variations across key demographic characteristics.Conclusions:HIWL, which communicates clear, non-quantitative messages about high levels of nutrients of concern, demonstrated the greatest efficacy to decrease the perceived healthfulness of a sweetened fruit drink across countries. This effect was similar across demographic characteristics.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".