Food Choice Under Five Front-of-Package Nutrition Label Conditions: An Experimental Study Across 12 Countries
Bibliographic record
Abstract
Objectives. To determine which front-of-package label (out of 5 formats) is most effective at guiding consumers toward healthier food choices. Methods. Respondents from Argentina, Australia, Bulgaria, Canada, Denmark, France, Germany, Mexico, Singapore, Spain, the United Kingdom, and the United States took part in the Front-of-Pack International Comparative Experiment between April and July 2018. Respondents were shown foods of varying nutritional quality (with no label on package) and selected which they would be most likely to purchase. The same choice sets were then shown again with 1 of 5 randomly allocated labels on package (Health Star Rating (HSR), Multiple Traffic Lights (MTL), Nutri-Score, Reference Intakes, or Warning Label). We calculated an improvement score (from 11 100 valid responses) to identify the extent to which the labels produced healthier choices. Results. The most effective labels were the Nutri-Score and the MTL (mean improvement score = 0.09; 95% confidence interval [CI] = 0.07, 0.11), then the Warning Label (0.06; 95% CI = 0.04, 0.08), the HSR (0.05; 95% CI = 0.03, 0.07), and lastly the Reference Intakes (0.04; 95% CI = 0.02, 0.04). Conclusions. Well-designed, salient, and intuitive front-of-package labels can be effective on a global scale. Their impact is not bound to the country from which they originate.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".