Healthy Kai (Food) Checker Web-Based Tool to Support Healthy Food Policy Implementation: Development and Usability Study
Notice bibliographique
Résumé
BACKGROUND: Public health programs and policies can positively influence food environments. In 2016, a voluntary National Healthy Food and Drink Policy was released in New Zealand to improve the healthiness of food and drinks for hospital staff and visitors. However, no resources were developed to support policy implementation. OBJECTIVE: This study aimed to design, develop, and test a new web-based tool to support food providers implementing the National Healthy Food and Drink Policy in New Zealand. METHODS: The Double Diamond model, a structured framework with 4 design phases, was used to design and develop a web-based tool. Findings from our previous research, such as (1) systematic review of barriers and facilitators to workplace healthy food policy implementation; (2) scoping review of current tools and resources available in New Zealand, Australia, and Canada; (3) interviews with food providers and public health nutrition professionals; and (4) food and drink availability audit results in New Zealand hospitals were used in the "Discover" (understanding of current gaps) and "Define" (prioritizing functions and features) phases. Subsequent phases focused on generating ideas, creating prototypes, and testing a new web-based tool using Figma, a prototyping tool. During the "Develop" phase, project stakeholders (11 public health nutrition professionals) provided feedback on the basic content outline of the initial low-fidelity prototype. In the final "Deliver" phase, a high-fidelity prototype resembling the appearance and functionality of the final tool was tested with 3 end users (public health nutrition professionals) through interactive interviews, and user suggestions were incorporated to improve the tool. RESULTS: A new digital tool, Healthy Kai (Food) Checker-a searchable database of packaged food and drink products that classifies items according to the Policy's nutritional criteria-was identified as a key tool to support Policy implementation. Of 18 potential functions and features, 11 were prioritized by the study team, including basic and advanced searches for products, sorting list options, the ability to compile a list of selected products, a means to report products missing from the database, and ability to use on different devices. Feedback from interview participants was that the tool was easy to use, was logical to navigate, and had an appealing color scheme. Suggested visual and usability improvements included ensuring that images represented the diverse New Zealand population, reducing unnecessary clickable elements, adding information about the free registration option, and including more frequently asked questions. CONCLUSIONS: Comprehensive research informed the development of a new digital tool to support implementation of the National Healthy Food and Drink Policy. Testing with end users identified features that would further enhance the tool's acceptability and usability. Incorporation of more functions and extending the database to include products classified according to the healthy school lunches program policy in the same database would increase the tool's utility.
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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 | 0,016 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».