Attitudes Toward a Proposed GPS-Based Location Tracking Smartphone App for Improving Engagement in HIV Care Among Pregnant and Postpartum Women in South Africa: Focus Group and Interview Study
Bibliographic record
Abstract
BACKGROUND: Peripartum women living with HIV in South Africa are at high risk of dropping out of care and are also a particularly mobile population, which may impact their engagement in HIV care. With the rise in mobile phone use worldwide, there is an opportunity to use smartphones and GPS location software to characterize mobility in real time. OBJECTIVE: The aim of this study was to propose a smartphone app that could collect individual GPS locations to improve engagement in HIV care and to assess potential users' attitudes toward the proposed app. METHODS: We conducted 50 in-depth interviews (IDIs) with pregnant women living with HIV in Cape Town and Johannesburg, South Africa, and 6 focus group discussions (FGDs) with 27 postpartum women living with HIV in Cape Town. Through an open-ended question in the IDIs, we categorized "positive," "neutral," or "negative" reactions to the proposed app and identified key quotations. For the FGD data, we grouped the text into themes, then analyzed it for patterns, concepts, and associations and selected illustrative quotations. RESULTS: In the IDIs, the majority of participants (76%, 38/50) responded favorably to the proposed app. Favorable comments were related to the convenience of facilitated continued care, a sense of helpfulness on the part of the researchers and facilities, and the difficulties of trying to maintain care while traveling. Among the 4/50 participants (8%) who responded negatively, their comments were primarily related to the individual's responsibility for their own health care. The FGDs revealed four themes: facilitating connection to care, informed choice, disclosure (intentional or unintentional), and trust in researchers. CONCLUSIONS: Women living with HIV were overwhelmingly positive about the idea of a GPS-based smartphone app to improve engagement in HIV care. Participants reported that they would welcome a tool to facilitate connection to care when traveling and expressed trust in researchers and health care facilities. Within the context of the rapid increase of smartphone use in South Africa, these early results warrant further exploration and critical evaluation following real-world experience with the app.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".