A five-country study of front- and back-of-package nutrition label awareness and use: patterns and correlates from the 2018 International Food Policy Study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to identify correlates of nutrition label awareness and use, particularly subgroup differences among consumers. Two label types were assessed: (1) nutrition facts tables (NFt) in Australia, Canada, Mexico, UK, and USA and (2) front-of-package (FOP) labels, including mandatory Guideline Daily Amounts (Mexico), voluntary Health Star Ratings (Australia) and voluntary Traffic Lights (UK). DESIGN: 21 586) and completed online surveys in November-December 2018. Linear regression and generalised linear mixed models examined differences in label use and awareness between countries and label type based on sociodemographic, knowledge-related and dietary characteristics. SETTING: Australia, Canada, Mexico, UK and USA. PARTICIPANTS: Adults (≥18 years). RESULTS: Respondents from the USA, Canada and Australia reported significantly higher NFt use and awareness than those in Mexico and the UK. Mexican respondents reported the highest level of FOP label awareness, whereas UK respondents reported the highest FOP label use. NFt use was higher among females, 'minority' ethnic groups, those with higher nutrition knowledge and respondents with 'adequate literacy' compared with those with 'high likelihood of limited literacy'. FOP label use was higher among those with a 'high likelihood of limited literacy' compared with 'adequate literacy' across countries. CONCLUSIONS: Lower use of mandatory Guideline Daily Amount labels compared with voluntary FOP labelling systems provides support for Mexico's decision to switch to mandatory 'high-in' warning symbols. The patterns of consumer label use and awareness across sociodemographic and knowledge-related characteristics suggest that simple FOP labels may encourage broader use across countries.
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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) | 0.000 | 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".