‘You're only there on the phone’? A qualitative exploration of community, affect and agential capacity in HIV self‐testing using a smartphone app
Bibliographic record
Abstract
Mobile health (mHealth) technologies for HIV care are developed to provide diagnostic support, health education, risk assessment and self-monitoring. They aim to either improve or replace part of the therapeutic relationship. Part of the therapeutic relationship is affective, with the emergence of feelings and emotion, yet little research on mHealth for HIV care focuses on affect and HIV testing practices. Furthermore, most of the literature exploring affect and care relations with the introduction of mHealth is limited to the European and Australian context. This article explores affective dimensions of HIV self-testing using a smartphone app strategy in Cape Town, South Africa and Montréal, Canada. This study is based on observation notes, 41 interviews and 1 focus group discussion with study participants and trained HIV healthcare providers from two quantitative studies evaluating the app-based self-test strategy. Our paper reveals how fear, apathy, judgement, frustration and comfort arise in testing encounters using the app and in previous testing experiences, as well as how this relates to care providers and test materials. Attending to affective aspects of this app-based self-testing practice makes visible certain affordances and limitations of the app within the therapeutic encounter and illustrates how mHealth can contribute to HIV care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".