Evaluation of a Curriculum-Based Nutrition Education Intervention Protocol in Elementary Schools: Nonrandomized Feasibility Study
Notice bibliographique
Résumé
Background: Improving children's food literacy through school-based interventions can support developing healthy eating habits. However, teachers lack appropriate resources, time, and training to provide nutrition education in schools. Serious games, which are games designed for a purpose other than entertainment, have been demonstrated to improve children's food literacy and dietary intake and can address the barriers teachers face in providing nutrition education. Foodbot Factory (Arcand Lab) is a nutrition education intervention that is aligned with curricula and uses a serious game to provide nutrition education to students. Further evidence is needed to understand how serious games, including Foodbot Factory, can be researched in schools to support nutrition education. Objective: The objective of this study was to evaluate the feasibility of a research study protocol that implements the curriculum-based nutrition education intervention Foodbot Factory into a real-world classroom setting. The evaluation of the protocol included study processes, resources, and management feasibility outcomes, as well as a preliminary assessment of scientific outcomes relevant to the intervention. Methods: A nonrandomized study determined the feasibility of intervention implementation. Grade 4 and 4/5 classrooms were assigned to have nutrition education lessons for 5 days with either the Foodbot Factory or a control intervention. Outcomes were assessed in 4 feasibility domains of study processes (eg, recruitment and attrition rates), resources (eg, time taken to deliver the intervention), and management (eg, challenges with intervention delivery), and a preliminary assessment of scientific outcomes pertaining to the acceptability and impacts of the interventions. These outcomes were captured in semistructured field notes completed by study staff and a Nutrition Attitudes and Knowledge questionnaire and acceptability questionnaire completed by participants. Data were analyzed descriptively and using a paired t test to assess within-group changes in nutrition knowledge. Results: In total, 4 classrooms participated in the feasibility study, with varying recruitment rates for schools (3/20, 15%), classrooms (4/4, 100%), parents (54/102, 53%), and children (49/54, 91%). The time required to implement the research protocol, including data collection and lesson plans, was sufficient and management of the intervention implementation was overall successful. Some challenges were experienced with classroom management during data collection, specifically with electronic data collection. After the intervention, participants reported a positive affective experience (26/41, 63%) and learning something new about healthy eating (31/41, 76%). Participants in both study groups improved their nutrition knowledge, but the changes were not statistically significant. The Foodbot Factory group had a statistically significant improvement in their knowledge of vegetables and fruit (P=.04) and protein foods (P=.03). Conclusions: These findings indicate that the study protocol is feasible to implement and evaluate Foodbot Factory in a representative sample with select modifications to improve recruitment and data collection procedures.
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 | 0,135 | 0,118 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,004 |
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 ».