Moments of Uncertainty: Exploring How an App-Based Oral HIV Self-Testing Strategy Fits in Communities ‘Living Under’ HIV Risk
Bibliographic record
Abstract
Feasibility and acceptability research for HIV self-testing (HIVST) often emphasises the importance of good test conduct and correct test interpretation for knowing one’s HIV result while overlooking the ways in which different uncertainties and meanings emerge around testing. Using empirical examples from a quantitative study assessing an app-based strategy in Cape Town, South Africa, this research article explores the practice of HIVST and how people deal with uncertainties while using the app in question, named ‘HIVSmart!’. We use the concept of ‘living under’ to explore the practices of HIV testing for those who fit the definition of being ‘at risk’ of HIV (note that an individual’s HIV status must be unknown in order for them to fit this definition) and to understand how an app-based HIVST strategy fits within these practices. We show how the app and oral self-test—as well as knowledge around HIV risk behaviours, comparisons between different testing methods, and the guidance and presence of healthcare staff—alleviate as well as generate uncertainty and constitute HIV status as an ongoing process. The effective implementation of new strategies for HIVST requires consideration of multiple aspects of the testing process, including local understandings of HIV risk, access to healthcare staff, and the meaning of certain test methods within a particular context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".