First-time Mothers’ Understanding and Use of a Pregnancy and Parenting Mobile App (The Baby Buddy App): Qualitative Study Using Appreciative Inquiry
Notice bibliographique
Résumé
BACKGROUND: Internationally, there is increasing emphasis on early support for pregnant women to optimize the health and development of mothers and newborns. To increase intervention reach, digital and app-based interventions have been advocated. There are growing numbers of pregnancy health care apps with great variation in style, function, and objectives, but evidence about impact on pregnancy well-being and behavior change following app interaction is lacking. This paper reports on the qualitative arm of the independent multicomponent study exploring the use and outcomes of first-time mothers using the Baby Buddy app, a pregnancy and parenting support app, available in the National Health Service App Library and developed by a UK child health and well-being charity, Best Beginnings. OBJECTIVE: This study aims to understand when, why, and how first-time mothers use the Baby Buddy app and the perceived benefits and challenges. METHODS: This paper reports on the qualitative arm of an independent, longitudinal, mixed methods study. An Appreciative Inquiry qualitative approach was used with semistructured interviews (17/60, 28%) conducted with new mothers, either by telephone or in a focus group setting. First-time mothers were recruited from 3 study sites from across the United Kingdom. Consistent with the Appreciative Inquiry approach, mothers were prompted to discuss what worked well and what could have been better regarding their interactions with the app during pregnancy. Thematic analysis was used, and findings are presented as themes with perceived benefits and challenges. RESULTS: The main benefit, or what worked well, for first-time mothers when using the app was being able to access new information, which they felt was reliable and easy to find. This led to a feeling of increased confidence in the information they accessed, thus supporting family and professional communication. The main challenge was the preference for face-to-face information with a health care professional, particularly around specific issues that they wished to discuss in depth. What could have been improved included that there were some topics that some mothers would have preferred in more detail, but in other areas, they felt well-informed and thus did not feel a need to seek additional information via an app. CONCLUSIONS: Although this study included a small sample, it elicited rich data and insights into first-time mothers' app interactions. The findings suggest that easily accessible pregnancy information, which is perceived as reliable, can support first-time mothers in communicating with health care professionals. Face-to-face contact with professionals was preferred, particularly to discuss specific and personalized needs. Further studies on maternal and professional digital support preferences after the COVID-19 global pandemic and how they facilitate antenatal education and informed decision-making are recommended, particularly because digital solutions remain as a key element in pregnancy and early parenting care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1017/S1463423618000294.
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 | 0,014 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,005 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».