"If they had a place to live, they would be taking medication": a qualitative study identifying strategies for engaging street-connected young people in the HIV prevention-care continuum in Kenya
Bibliographic record
Abstract
INTRODUCTION: Street-connected young people (SCY) experience structural and social barriers to engaging in the HIV prevention-care continuum. We sought to elicit recommendations for interventions that may improve SCY's engagement along the HIV prevention-care continuum from healthcare providers, policymakers, community members and SCY in Kenya. METHODS: This qualitative study was conducted in Uasin Gishu, Trans Nzoia, Bungoma, Nakuru and Kitale counties in Kenya between May 2017 and September 2018 to explore and describe the public perceptions of, and proposed and existing responses to, the phenomenon of SCY. This secondary analysis focuses on a subset of data interviews that investigated SCY's healthcare needs in relation to HIV prevention and care. We conducted 41 in-depth interviews and seven focus group discussions with 100 participants, of which 43 were SCY. In total, 48 participants were women and 52 men. RESULTS: Our analysis resulted in four major themes corresponding to stages in the HIV prevention-care continuum for key populations. We identified the need for an array of strategies to engage SCY in HIV prevention and testing services that are patient-centred and responsive to the diversity of their circumstances. The use of pre-exposure prophylaxis was a biomedical prevention strategy that SCY and healthcare providers alike stressed the need to raise awareness around and access to for SCY. Several healthcare providers suggested peer-based approaches for engaging SCY throughout the continuum. However, SCY heavily debated the appropriateness of using peer-based methods. Structural interventions, such as the provision of food and housing, were suggested as strategies to improve antiretroviral therapy adherence. CONCLUSIONS: This study identified contextually relevant interventions that should be adapted and piloted for use with SCY. Education and sensitization of SCY and healthcare providers alike were identified as possible strategies, along with affordable housing and anti-poverty strategies as cash transfers and provision of food. Peer-based interventions are a clear option but require SCY-specific adaptation to be implemented effectively.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".