Using a mixed‐methods approach to adapt an HIV stigma reduction intervention to address intersectional stigma faced by men who have sex with men in Ghana
Bibliographic record
Abstract
INTRODUCTION: In Ghana, men who have sex with men (MSM) are estimated to be 11 times more likely to be living with HIV than the general population. Stigmas at the intersection of HIV, same-sex and gender non-conformity are potential key drivers behind this outsized HIV disease burden. Healthcare workers (HCWs) are essential to HIV prevention, care and treatment and can also be sources of stigma for people living with HIV and MSM. This article describes the process and results of adapting an evidence-based HIV stigma-reduction HCW training curriculum to address HIV, same-sex and gender non-conformity stigma among HCWs in the Greater Accra and Ashanti regions, Ghana. METHODS: Six steps were implemented from March 2020 to September 2021: formative research (in-depth interviews with stigma-reduction trainers [n = 8] and MSM living with HIV [n = 10], and focus group discussions with HCWs [n = 8] and MSM [n = 8]); rapid data analysis to inform a first-draft adapted curriculum; a stakeholder adaptation workshop; triangulation of adaptation with HCW baseline survey data (N = 200) and deeper analysis of formative data; iterative discussions with partner organizations for further refinement; external expert review; and final adaptation with the teams of HCWs and MSM being trained to deliver the curriculum. RESULTS: Key themes emerging under four immediately actionable drivers of health facility intersectional stigma (awareness, fear, attitudes and facility environment) informed the adaptation of the HIV training curriculum. Based on the findings, existing curriculum exercises were placed in one of four categories: (1) Expand-existing exercises that needed modifications to incorporate deeper MSM and gender non-conformity stigma content; (2) Generate-new exercises to fill gaps; (3) Maintain-exercises to keep with no modifications; and (4) Eliminate-exercises that could be dropped given training time constraints. New exercises were developed to address gender norms, the belief that being MSM is a mental illness and stigmatizing attitudes towards MSM. CONCLUSIONS: Getting to the "heart of stigma" requires understanding and responding to both HIV and other intersecting stigma targeting sexual and gender diversity. Findings from this study can inform health facility stigma reduction programming not only for MSM, but also for other populations affected by HIV-related and intersectional stigma in Ghana and beyond.
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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 | 0.025 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".