HIV Self-Testing Implementation, Distribution and Use Among Female Sex Workers in Cotonou, Benin: A Qualitative Evaluation of Acceptability and Feasibility
Bibliographic record
Abstract
Abstract Background In Benin, the burden of HIV is disproportionately high among female sex workers (FSWs). HIV testing and knowledge of status are starting points for HIV treatment and prevention interventions. Despite the importance given to testing services in HIV control, its uptake among FSWs remains suboptimal in Benin. HIV self-testing (HIVST) may be useful for increasing testing rates in FSWs. Methods We conducted a pilot study of the distribution of saliva-based HIVST among FSWs in Cotonou and its surroundings, Benin. The HIVST promotion and distribution model included three complementary strategies: community-based, facility-based and secondary distribution. In this qualitative study, we explored the elements influencing HIVST implementation, distribution and use among FSWs. We assessed HIVST acceptability and feasibility in this population. We conducted 29 semi-structured individual interviews with FSWs. Data were interpreted with a thematic analysis method, using the Theoretical Domains Framework. Results Only two FSWs (6.9%) were aware of HIVST before participating in the study. All participants were interested in using HIVST if available in Benin. Many advantages of HIVST were mentioned, including: autonomy, privacy, accessibility, time saving, and the fact that it is a painless test. Barriers to the use of HIVST included: the fear of unreliability, the lack of psychological support and medical follow-up and the possibility of result dissimulation. Participants thought HIVST was easy to use without assistance. HIVST enabled linkage to care for a few FSWs in denial of their HIV-positive status. No case of suicide or violence associated with HIVST was reported. HIVST secondary distribution within FSWs social network was well received. FSWs’ boyfriends and clients showed interest in using the device. Some FSWs reported using HIVST to practice serosorting or to guide their decisions regarding condom use. Conclusions Our study shows a very high level of acceptability for HIVST among FSWs in Cotonou and its surroundings. Results also demonstrate the feasibility of implementing HIVST distribution in Benin. HIVST should be implemented in Benin quickly and free of charge for all individuals at risk of HIV. HIVST offer should be integrated with comprehensive sexual health and prevention services.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".