Consumers’ Implicit and Explicit Recall, Understanding and Perceptions of Products with Nutrition-Related Messages: An Online Survey
Bibliographic record
Abstract
This study aimed to assess consumers' implicit and explicit recall, understanding and perceptions of products with a nutrition claim and a symbol depicting 'health,' and to determine whether these perceptions differed among Nutrition Facts table (NFt) users vs. nonusers. In an online survey, participants (n = 1997) were randomized to one of eight conditions in a 2 × 2 × 2 factorial design, consisting of a label with a claim (present/absent) a heart-shaped symbol depicting 'health' (present/absent) for a healthier or less healthy soup. Participants were shown a label for 10 s and asked whether they recalled seeing a claim. If participants answered yes, they were then asked to describe their response using open-ended questions. Participants also rated the product's perceived nutritional quality and purchase intentions using seven-point Likert scales. In the claim condition, most participants (75%) were able to recall the presence of a claim, while 12% incorrectly mentioned the presence of a claim when there was none. Claims likely attracted consumers' attention and increased perceived nutritional quality, although with limited influence among NFt users (23%). The symbol depicting 'health' did not enhance perceived nutritional quality or purchase intentions. Although most participants (77%) made their decisions implicitly using the front of labels, those who used the NFt had a better understanding of the nutritional quality of products.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".