Staff Perceptions of Preimplementation Barriers and Facilitators to a Mobile Health Antiretroviral Therapy Adherence Counseling Intervention in South Africa: Qualitative Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".