Can kids identify unprocessed fruit as healthier than an ultra-processed sugar-sweetened beverage? Functional versus self-reported nutrition knowledge and dietary intake among youth from six countries: findings from the International Food Policy Study
Notice bibliographique
Résumé
BACKGROUND: Consumption of ultra-processed foods is associated with a range of poor dietary and health outcomes. Although lower nutrition knowledge is associated with higher consumption of ultra-processed foods, few studies have directly compared nutrition knowledge among youth from countries with different food environments and nutrition policies. This study examined whether youth could identify differences in nutritional quality between a commonly consumed ultra-processed and unprocessed food. METHODS: Cross-sectional surveys were conducted with youth aged 10-17 (n = 12,489) from Australia, Canada, Chile, Mexico, United Kingdom (UK), and United States (US) as part of the 2020 International Food Policy Study. Participants were shown images of two products in random order, corresponding to "unprocessed or minimally processed" (apple) and "ultra-processed" (apple fruit drink) foods under NOVA classification system, and asked to rate the healthiness of each. Respondents who rated the apple higher than the apple fruit drink were assigned a "correct" score. Regression models examined differences in "correct" responses by country, perceived nutrition knowledge, perceived diet healthiness, intake of fruits/vegetables, and intake of less healthy foods. RESULTS: Mexican (96.5%) and Chilean (94.3%) youth were most likely to correctly identify the unprocessed apple as "healthier" than the ultra-processed apple fruit drink, whereas US youth were the least likely (79.6%, p < 0.001 for all). Perceived nutrition knowledge was inversely associated with correct scores (p < 0.001). Youth who reported the highest (AOR: 0.43, p < 0.001) and lowest (AOR: 0.57, p < 0.05) categories of perceived diet healthiness had the lowest odds of correct responses. Higher intake of both less healthy foods (AOR: 0.70, p < 0.001) and fruits/vegetables (AOR: 0.87, p < 0.001) were associated with lower odds of correct responses. CONCLUSIONS: Across countries, 5-20% of youth were unable to correctly identify an unprocessed fruit as 'healthier' than an ultra-processed fruit drink, with notable country differences. Further research is needed to examine differences for a broader range of foods and levels of processing. Education campaigns should ensure that young people have basic knowledge about the relative dietary quality of commonly consumed foods, particularly in the US. Discrepancies between perceived and objective nutrition knowledge additionally highlight the need for objective measures of knowledge to be included in assessments.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».