Staff Perceptions of Preimplementation Barriers and Facilitators to a Mobile Health Antiretroviral Therapy Adherence Counseling Intervention in South Africa: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: South Africa adopted a universal test and treatment program for HIV infection in 2015. The standard of care that people living with HIV receive consists of 3 sessions of readiness counseling delivered by lay counselors (LCs). In the largest antiretroviral therapy (ART) program worldwide, effective and early HIV and ART education and support are key for ensuring ART adoption, adherence, and retention in care. Having LCs to deliver readiness counseling allows for the wide task-sharing of this critical activity but carries the risks of loss of standardization, incomplete content delivery, and inadequate monitoring and supervision. Systems for ensuring that a minimum standard of readiness counseling is delivered to the growing number of people living with HIV are essential in the care cascade. In resource-constrained, high-burden settings, mobile health (mHealth) apps may potentially offer solutions to these treatment gaps by providing content structure and delivery records. OBJECTIVE: This study aims to explore, at a large Cape Town-based nonprofit HIV care organization, the staff's perceived preimplementation barriers and facilitators of an mHealth intervention (Masivukeni) developed as a structured app for ART readiness counseling. METHODS: Masivukeni is a laptop-based app that incorporates written content, graphics, short video materials, and participant activities. In total, 20 participants were included in this study. To explore how an mHealth intervention might be adopted across different staff levels within the organization, we conducted 7 semistructured interviews (participants: 7/20, 35%) and 3 focus groups (participants in 2 focus groups: 4/20, 20%; participants in 1 focus group: 3/20, 15%) among LCs, supervisors, and their managers. In total, 20 participants were included in this study. Interviews lasted approximately 60 minutes, and focus groups ranged from 90 to 120 minutes. The Consolidated Framework for Implementation Research was used to explore the perceived implementation barriers and facilitators of the Masivukeni mHealth intervention. RESULTS: Several potential facilitators of Masivukeni were identified. Multimedia and visual elements were generally regarded as aids in content delivery. The interactive learning components were notably helpful, whereas facilitated updates to the adherence curriculum were important to facilitators and managers. The potential to capture administrative information regarding LC delivery and client logging was regarded as an attractive feature. Barriers to implementation included security risks and equipment costs, the high volume of clients to be counseled, and variable computer literacy among LCs. There was uncertainty about the app's appeal to older clients. CONCLUSIONS: mHealth apps, such as Masivukeni, were perceived as being well placed to address some of the needs of those who deliver ART adherence counseling in South Africa. However, the successful implementation of mHealth apps appeared to be dependent on overcoming certain barriers in this setting.
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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 | 0,007 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».