Understanding and use of food labeling systems among Whites and Latinos in the United States and among Mexicans: Results from the International Food Policy Study, 2017
Bibliographic record
Abstract
BACKGROUND: Obesity and chronic diseases could be prevented through improved diet. Most governments require at least one type of food labeling system on packaged foods to communicate nutrition information and promote healthy eating. This study evaluated adult consumer understanding and use of nutrition labeling systems in the US and Mexico, the most obese countries in the world. METHODS: Adults from online consumer panels in the US (Whites n = 2959; Latinos n = 667) and in Mexico (n = 3533) were shown five food labeling systems: 1. Nutrition Facts Table (NFT) that shows nutrients of concern per serving; 2. Guideline Daily Amounts (GDA) that shows levels of nutrients of concern; 3. Multiple Traffic-Light (MTL) that color codes each GDA nutrient (green = healthy; yellow = moderately unhealthy; red = unhealthy); 4. Health Star Rating System (HSR) that rates foods on a single dimension of healthiness; 5. Warning Label (WL) with a stop sign for nutrients present in unhealthy levels. Participants rated each label on understanding ("easy"/"very easy to understand" vs "difficult"/"very difficult to understand"), and, for NFTs and GDAs, frequency of use ("sometimes"/"often" vs "never"). Mixed logistic models regressed understanding and frequency of use on indicators of labeling systems (NFT = ref), testing for interactions by ethnicity (US Latinos, US Whites, Mexicans), while controlling for sociodemographic and obesity-related factors. RESULTS: Compared to the NFT, participants reported greater understanding of the WL (OR = 4.8; 95% CI = 4.4-5.3) and lower understanding of the HSR (OR = 0.34, 95% CI = 0.31-0.37) and the MTL (OR = 0.56, 95% CI = 0.52-0.61), with similar patterns across ethnic subgroups. Participants used GDAs less often than NFTs (OR = 0.48; 95%CI = 0.41-0.55), with the greatest difference among US Whites (OR = 0.10; 95%CI = 0.07-0.14). CONCLUSIONS: Understanding and use of the GDA was similar to that of the NFT. Whites, Latinos, and Mexicans consistently reported the best understanding for WLs, a FOPL that highlights unhealthfulness of a product. Therefore, a FOPL summary indicator, such as WLs, may be more effective in both the US and Mexico for guiding consumers towards informed food choices.
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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 | 0.000 | 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".