The MomConnect Nurses and Midwives Support Platform (NurseConnect): A Qualitative Process Evaluation
Notice bibliographique
Résumé
BACKGROUND: Over the past decade, mobile health has steadily increased in low-income and middle-income countries. However, few platforms have been able to sustainably scale up like the MomConnect program in South Africa. NurseConnect was created as a capacity building component of MomConnect, aimed at supporting nurses and midwives in maternal and child health. The National Department of Health has committed to expanding NurseConnect to all nurses across the country, and an evaluation of the current user experience was conducted to inform a successful scale up. OBJECTIVE: This study aims to evaluate the perception and use of NurseConnect by nurses and midwives to produce feedback that can be used to optimize the user experience as the platform continues to scale up. METHODS: We conducted focus group discussions and in-depth interviews with 110 nurses and midwives from 18 randomly selected health care facilities across South Africa. Questions focused on mobile phone use, access to medical information and their experience with NurseConnect registration, as well as the content and different platforms. RESULTS: All participants had mobile phones and communication through calls and messaging was the main use in both personal and work settings. Of 110 participants, 108 (98.2%) had data-enabled phones, and the internet, Google, and apps (South African National Department of Health Guidelines, iTriage, Drugs.com) were commonly used, especially to find information in the work setting. Of 110 participants, 62 (56.4%) were registered NurseConnect users and liked the message content, especially listeriosis and motivational messages, which created behavioral change in some instances. The mobisite and helpdesk, however, were underutilized because of a lack of information surrounding these platforms. Some participants did not trust medical information from websites and had more confidence in apps, while others associated a "helpdesk" with a call-in service, not a messaging one. Many of the unregistered participants had not heard of NurseConnect, and some cited data and time constraints as barriers to both registration and uptake. CONCLUSIONS: Mobile and smartphone penetration was very high, and participants often used their phone to find medical information. The NurseConnect messages were well-liked by all registered participants; however, the mobisite and helpdesk were underutilized owing to a lack of information and training around these platforms. Enhanced marketing and training initiatives that optimize existing social networks, as well as the provision of data and Wi-Fi, should be explored to ensure that registration improves, and that users are active across all platforms.
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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 | 0,102 | 0,077 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,006 | 0,005 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».