Experimental study of front-of-package nutrition labels’ efficacy on perceived healthfulness of sugar-sweetened beverages among youth in six countries
Bibliographic record
Abstract
Front-of-package (FOP) nutrition labels have been proposed as a strategy to help limit sugar-sweetened beverage (SSB) consumption among youth. However, few studies have examined the efficacy of FOP labels in youth across different countries. A between-group experiment was conducted to examine the impact of FOP labels (no-label control, Health Star Rating, 'High in' Octagon, Guideline Daily Amount (GDA), Traffic Light, or Nutri-Score) on perceived healthfulness of an SSB. The study was conducted online in November-December 2019 with 10,762 children aged 10-17 from six countries: Australia, Canada, Chile, Mexico, the United Kingdom, and the United States. A binary logistic regression model tested the impacts of FOP label condition, country, and sociodemographic characteristics on participants' likelihood of perceiving the SSB to be Unhealthy. Compared to the control condition, participants in each of the five FOP label conditions were significantly more likely to perceive the SSB as Unhealthy (p < 0.002). The 'High in' Octagon label had the greatest impact on perceived healthfulness across five out of six countries, whereas the GDA and Nutri-Score labels demonstrated the lowest impact across all six countries. The impact of FOP labels was consistent across sex, age, race/ethnicity, and perceived income adequacy. FOP labels can significantly reduce the perceived healthfulness of SSBs among youth across multiple countries. The current study adds to the evidence that 'high in' labels, which use intuitive symbols such as the octagon 'stop sign', are the most efficacious labels for helping consumers identify foods high in nutrients of concern, including SSBs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".