Co-Designing an Infant Early Childhood Mental Health Mobile App for Early Childhood Education Teachers' Professional Development: Community-Based Participatory Research Approach
Notice bibliographique
Résumé
BACKGROUND: Many young children spend at least some time in early care and education programs, where they develop social-emotional skills that prepare them for future success. However, young children may exhibit behavioral challenges in these settings, negatively impacting their social-emotional development. It is critical that the early childhood workforce is prepared to support young children's burgeoning social-emotional skills to address challenging behaviors in early care and education classrooms. Infant and early childhood mental health consultation is an evidence-informed approach for increasing teachers' skills for managing young children's emotions and behaviors. One mechanism to increase teachers' access and use of the infant and early childhood mental health consultation programs is through on-demand mobile apps. OBJECTIVE: This study aims to investigate 2 primary objectives: to document the development of the Jump Start on the Go (JS Go) app through community-based participatory research (CBPR) methodologies, and to evaluate and refine the app based on early childhood education (ECE) teacher feedback using a mixed methods assessment approach. METHODS: This study used a community-based participatory research approach to design and evaluate the effectiveness of the JS Go app across 3 phases. In phase 1, a description of how the JS Go app was developed using CBPR principles is provided. In phase 2, teachers (n=12) were interviewed after reviewing mockups of the JS Go app to gather feedback about the interface and usefulness of the app to current and new teachers. Rapid qualitative analysis generated themes to inform phase 3 (n=31) of the study. RESULTS: Phase 2 findings suggested that teachers viewed the app as aesthetically pleasing with concise information, but there were design and content features that needed to be refined to improve ease of use for accessing content. Teachers also described the app as beneficial and useful to both current and new ECE teachers and identified it as a tool to support sustainability for the use of JS practices. In phase 3, teachers rated the JS Go app favorably across all mHealth (mobile health) App Usability Questionnaire dimensions, including interface satisfaction (mean 6.12 on a 7-point scale), ease of use (mean 5.56), and usefulness (mean 5.37). Despite positive usability ratings, teachers expressed less certain intentions to adopt the app, scoring near the midpoint on the Technology Acceptance Model Instrument-Fast Form's predicted future use scale (mean 1.60, -4 to +4-point scale). Implications for how the findings were used to make adaptions to the app are discussed. The next steps for testing the efficacy of the app in a randomized control trial are described. CONCLUSIONS: ECE teachers have overall positive perceptions about the value of the JS Go app. Future research will need to test the efficacy of the app for increasing and sustaining teacher's use of JS practices.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,009 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».