Health Care Professionals’ Attitudes Toward, and Experiences of Using, a Culture-Sensitive Smartphone App for Women with Gestational Diabetes Mellitus: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: The increasing prevalence of gestational diabetes mellitus (GDM) among women of different ethnic backgrounds provides new challenges for health care professionals, who often find it difficult to provide information about the management of this disease to such individuals. Mobile health (mHealth) may act as a useful tool for blood sugar control and care process enhancement. However, little is known about health care professionals' experiences and attitudes toward the use of mHealth for women with GDM. OBJECTIVE: The aim of this study was to explore how health care professionals perceived the provision of care to pregnant women who managed their GDM using the culture-sensitive Pregnant+ app in a randomized controlled trial. METHODS: Individual interviews with 9 health care professionals providing care for women with GDM were conducted. Braun and Clark's method of thematic content analysis inspired the analysis. This study included health care professionals who were primarily responsible for providing care to participants with GDM in the Pregnant+ randomized controlled trial at 5 diabetes outpatient clinics in Oslo, Norway. RESULTS: Health care professionals perceived mHealth, particularly the Pregnant+ app, as an appropriate tool for the care of women with GDM, who were described as individuals comprising a heterogeneous, motivated group that could be easily approached with health-related information. Some participants reported challenges with respect to provision of advice to women with different food cultures. The advantages of the Pregnant+ app included provision of information that women could access at home, the information provided being perceived as trustworthy by health care professionals, the culture sensitivity of the app, and the convenience for women to register blood sugar levels. Technical problems, particularly those associated with the automatic transfer of blood glucose measurements, were identified as the main barrier to the use of the Pregnant+ app. Strict inclusion criteria and the inclusion of participants who could not speak Norwegian were the main challenges in the recruitment process for the randomized controlled trial. CONCLUSIONS: The findings of this study suggest that mHealth is a useful tool to enhance the care provided by health care professionals to women with GDM. Future mobile apps for the management of GDM should be developed by a trustworthy source and in cooperation with health care professionals. They should also be culture sensitive and should not exhibit technical problems.
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 | 0,012 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».