Willingness to use and distribute HIV self-test kits to clients and partners: A qualitative analysis of female sex workers’ collective opinion and attitude in Côte d’Ivoire, Mali, and Senegal
Bibliographic record
Abstract
BACKGROUND: In West Africa, female sex workers are at increased risk of HIV acquisition and transmission. HIV self-testing could be an effective tool to improve access to and frequency of HIV testing to female sex workers, their clients and partners. This article explores their perceptions regarding HIV self-testing use and the redistribution of HIV self-testing kits to their partners and clients. METHODS: Embedded within ATLAS, a qualitative study was conducted in Côte-d'Ivoire, Mali, and Senegal in 2020. Nine focus group discussions were conducted. A thematic analysis was performed. RESULTS: A total of 87 participants expressed both positive attitudes toward HIV self-testing and their willingness to use or reuse HIV self-testing. HIV self-testing was perceived to be discreet, confidential, and convenient. HIV self-testing provides autonomy from testing by providers and reduces stigma. Some perceived HIV self-testing as a valuable tool for testing their clients who are willing to offer a premium for condomless sex. While highlighting some potential issues, overall, female sex workers were optimistic about linkage to confirmatory testing following a reactive HIV self-testing. Female sex workers expressed positive attitudes toward secondary distribution to their partners and clients, although it depended on relationship types. They seemed more enthusiastic about secondary distribution to their regular/emotional partners and regular clients with whom they had difficulty using condoms, and whom they knew enough to discuss HIV self-testing. However, they expressed that it could be more difficult with casual clients; the duration of the interaction being too short to discuss HIV self-testing, and they fear violence and/or losing them. CONCLUSION: Overall, female sex workers have positive attitudes toward HIV self-testing use and are willing to redistribute to their regular partners and clients. However, they are reluctant to promote such use with their casual clients. HIV self-testing can improve access to HIV testing for female sex workers and the members of their sexual and social network.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| 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".