Effect of fat claims on consumer perceptions about product helpfulness for weight management
Bibliographic record
Abstract
Participants from an online consumer panel (n=3000) were randomly assigned to 1 of 9 treatment conditions in a 3X3 factorial design for fat claim (no claim, trans fat free or low fat claim) and nutrition information (no Nutrition Facts Table (NFT), low fat/low calorie, or low fat/high calorie), for both a breakfast cereal and frozen meal package. Compared to products with high calorie NFTs, products with low calorie NFTs were rated by participants as lower in calories, whether or not the product had a fat claim. Compared to those who did not see a claim or NFT, participants who saw frozen meals with a trans fat free claim and no NFT rated products as healthier and more helpful for weight management, while participants who saw breakfast cereals with only a low fat claim rated products as less healthy and participants were less willing to purchase these products. When shown the NFT, the likeliness that participants would favour low over high calorie products increased for products with fat claims compared to products without claims. These results demonstrate that consumers use fat claims in conjunction with the NFT to inform decisions, rather than the claim alone. Thus, fat claims may help consumers, rather than mislead them, to choose products that are better for weight management. However, while fat claims may help with decision‐making, they have been shown to negatively affect consumers’ eating behaviours.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".