The impact of Nutri-Score on elderly consumers’ perceptions
Bibliographic record
Abstract
• The aging population in Italy faces health challenges related to diet. • NutriScore affects health perceptions and lowers taste expectations of food products. • Dietary regime generally increases perceived healthiness of products with NS. The growing aging population presents significant public health challenges worldwide, particularly in terms of nutrition and diet-related diseases. In Italy, where more than a quarter of the population is aged 65 and older, this issue is especially pressing. Many elderly individuals struggle to make nutritionally informed choices due to limited awareness and knowledge about food quality. This study examines the impact of the Nutri-Score (NS) label on the perceived healthiness of food products among Italian older adults. Applying a within-subjects experimental design, 557 elderly consumers, partially or fully responsible for household food shopping, evaluated five commonly consumed products—pasta, extra virgin olive oil, mozzarella cheese, canned tuna in olive oil, and milk chocolate bar—both with and without the NS label. Findings reveal that the NS significantly impacts health perceptions and decreases taste expectations across food products. Additionally, individual characteristics such as dietary habits and NS knowledge influence these changes. Policy makers and stakeholders should further explore the appropriateness of NS as an effective method to guide elderly consumers toward healthier food choices.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".