Experiences around testing and linkage to HIV care among gay, bisexual and other men who have sex with men: qualitative findings from the patient-centered care project in the lake Victoria region of Kenya
Bibliographic record
Abstract
Although the Kenyan Ministry of Health recognizes key populations in addressing the country's HIV epidemic, gay, bisexual, and other men who have sex with men (GBMSM) face challenges in HIV testing, linkage, and retention in care. This study aimed to inform the development of context-specific interventions to improve GBMSM's initiation in HIV care in Kenya. Drawing on in-depth semi-structured interview data from the Patient-Centered Care Project (PCCP), a qualitative study exploring the experiences of people with HIV (PWH) across the continuum of care, we focused on GBMSM aged ≥18 years with HIV in Homa Bay, Kisumu, and Siaya Counties. Data from 93 interviews conducted in 2017 (60 in- and 33 out-of-HIV care) were analyzed. Participants reflected on interpersonal (stigma, emotional support) and structural (distance of clinics, food and economic insecurity) challenges and identified valuable programs that could address these (GBMSM-focused support groups, economic opportunities, food supplementation, government support). These findings highlight how stigma (related to HIV and GBMSM identity) hinders access to HIV testing services and linkage to care. Creating inclusive healthcare environments and fostering opportunities for economic support and social connections are essential for improving engagement with HIV 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 | 0.007 | 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 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".