Unintended uses, meanings, and consequences: HIV self-testing among female sex workers in urban Uganda
Bibliographic record
Abstract
Female sex workers (FSWs) are at increased risk of HIV and face significant barriers to clinic-based HIV testing, including provider stigma and privacy constraints. HIV self-testing (HIVST) has been proven to significantly increase HIV testing among FSWs. Less is known, however, about how FSWs make meaning of oral-fluid HIV self-tests, and the unintended ways they use and understand this novel technology. From October 2016 to March 2017, we conducted 61 in-depth interviews with FSWs (n = 31) in Kampala, Uganda. Eligible participants were: female, ≥18 years, exchanged sex for money or goods, and had not recently tested for HIV. We used inductive coding to identify emerging themes and re-arranged these into an adapted framework. Unintended desirable ways FSWs described self-testing included as a means to test others, to bolster their reputation as a health-conscious sex worker, and to avoid bearing witness to suffering at health facilities. Unintended undesirable meanings ascribed to self-testing included misunderstandings about how HIV is transmitted (via saliva versus blood) and whether self-tests also test for other infections. HIVST can increase FSWs’ knowledge of their own HIV status and that of their sexual partners, but messaging and intervention design must address misunderstandings and misuses of self-testing.Trial registration: ClinicalTrials.gov identifier: NCT02846402.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".