Evaluating the impact of the Nutrition Facts table and front‐of‐pack nutrition rating systems on consumersˈ product healthiness evaluations
Bibliographic record
Abstract
Few studies have examined how consumers use the Nutrition Facts table (NFT) and front‐of‐pack nutrition rating systems (FOPS) when presented with one or both sets of nutrition information, and whether there is an interaction between the two systems. 337 online survey participants were randomized to rate the healthiness of a frozen meal under one of five FOPS conditions, with or without a NFT: 1) no‐FOPS, 2) a manufacturer or 3) a non‐profit style summary indicator system (SIS), or 4) a traffic light or 5) a percent daily value nutrient‐specific system (NSS). Mann‐Whitney U tests were used to evaluate rating differences. When consumers were shown the various FOPS without a NTF, the non‐profit SIS group gave higher healthiness ratings than the NSS groups; the no‐ FOPS and the manufacturer SIS groups gave higher ratings than the percent daily value group (P<0.05). Other group comparisons were not significant. When consumers were shown the various FOPS with a NFT, none of the FOPS groups ratings were significantly different. Between no‐NFT/NFT conditions, consumers in the no‐FOPS and SIS groups gave higher healthiness ratings without a NFT. These results suggest that when provided with additional nutrition information on a product, as in NSSs and NFTs, consumers incorporate this information into their healthiness evaluations. Supported by Dairy Farmers of Canada, AFMNet, & CCO/CIHR Training Grant(#53893)
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".