HIV Education, Empathy, and Empowerment (HIVE3): A Peer Support Intervention for Reducing Intersectional Stigma as a Barrier to HIV Testing among Men Who Have Sex with Men in Ghana
Bibliographic record
Abstract
Men who have sex with men (MSM) in Ghana remain at heightened risk of HIV infection, and face challenges in accessing HIV prevention and care services. Previous research in Ghana shows that MSM face intersectional stigma across ecological levels (family, peers, healthcare settings, and community level) and the criminalization of same-gender sexual behaviors in the country. To protect their wellbeing from exposure to stigma, many MSM avoid interactions with healthcare systems and services, which inadvertently inhibits their opportunities for early detection and treatment of HIV. Consequently, MSM in Ghana carry a disproportionate burden of HIV prevalence (18%) compared to the general population (2%), highlighting the need for culturally relevant processes in HIV/STI prevention, and care communication to optimize sexual health and wellness among MSM in Ghana. To this effect, we collaborated with community partners to use the Assessment, Decision, Adaptation, Production, Topical Experts, Training, Testing (ADAPT-ITT) model to modify a theory-driven smartphone-based peer support intervention to enhance its focus on intersectional stigma reduction, and improve HIV health-seeking behaviors among MSM, including HIV testing and linkage to care. We used the Dennis Peer Support Model to develop the peer support components (emotional, informational, and appraisal support) to increase peer social support, decrease social isolation, and minimize intersectional stigma effects on HIV-related healthcare-seeking behaviors. This paper shows the preliminary acceptability and effectiveness of employing culturally relevant techniques and communication strategies to provide secure peer support to improve HIV prevention and care among key populations in highly stigmatized environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".