Is it possible to recruit HIV self-test users for an anonymous phone-based survey using passive recruitment without financial incentives? Lessons learned from a pilot study in Côte d’Ivoire
Bibliographic record
Abstract
BACKGROUND: Due to the discreet and private nature of HIV self-testing (HIVST), it is particularly challenging to monitor and assess the impacts of this testing strategy. To overcome this challenge, we conducted a study in Côte d'Ivoire to characterize the profile of end users of HIVST kits distributed through the ATLAS project (AutoTest VIH, Libre d'Accéder à la connaissance de son Statut). Feasibility was assessed using a pilot phone-based survey. METHODS: The ATLAS project aims to distribute 221300 HIVST kits in Côte d'Ivoire from 2019 to 2021 through both primary (e.g., direct distribution to primary users) and secondary distribution (e.g., for partner testing). The pilot survey used a passive recruitment strategy-whereby participants voluntarily called a toll-free survey phone number-to enrol participants. The survey was promoted through a sticker on the HIVST instruction leaflet and hotline invitations and informal promotion by HIVST kit-dispensing agents. Importantly, participation was not financially incentivized, even though surveys focussed on key populations usually use incentives in this context. RESULTS: After a 7-month period in which 25,000 HIVST kits were distributed, only 42 questionnaires were completed. Nevertheless, the survey collected data from users receiving HIVST kits via both primary and secondary distribution (69% and 31%, respectively). CONCLUSION: This paper provides guidance on how to improve the design of future surveys of this type. It discusses the need to financial incentivize participation, to reorganize the questionnaire, the importance of better informing and training stakeholders involved in the distribution of HIVST, and the use of flyers to increase the enrolment of users reached through secondary distribution.
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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 | 0.182 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".