Multi-level intersectional stigma reduction intervention to increase HIV testing among men who have sex with men in Ghana: Protocol for a cluster randomized controlled trial
Bibliographic record
Abstract
Men with have sex with men (MSM) in Africa face high levels of stigma due to elevated HIV exposure (actual or perceived), same-sex practices, and gender non-conformity. These stigmas are documented barriers to HIV prevention and treatment. Most stigma-reduction interventions have focused on single-level targets (e.g., health care facility level [HCF]) and addressed one type of stigma (e.g., HIV), without engaging the multiple intersecting stigmas that MSM encounter. Determining the feasibility and acceptability of multi-level intervention of reducing intersectional stigma and estimating its efficacy on increasing HIV testing are needed.We proposed a mixed method study among MSM in Ghana. First, we will develop the intervention protocol using the Convergence Framework, which combines three interventions that were previously implemented separately in Ghana for reducing stigma at the HCF-level, increasing HIV testing at the peer group-level, and increasing peer social support at the individual-level. Then, we will conduct a cluster randomized controlled trial with four pairs of HCFs matched on staff size. HCFs within each pair are randomized to the HCF-level stigma-reduction intervention or control arm. MSM (n = 216) will be randomized to receive the group-level and individual-level interventions or standard of care control arm. MSM will be assigned to receive HIV testing at one of the HCFs that match their study assignment (intervention or control facility). The frequency of HIV testing between MSM in the study arms at 3 and 6 months will be compared, and the predictors of HIV testing uptake at the HCF, peer group and individual-levels will be assessed using multi-level regression models.These findings from this study will provide important evidence to inform a hybrid implementation-effectiveness trial of a public health intervention strategy for increasing HIV case detection among key populations in sub-Saharan African communities. Accurate information on HIV prevalence can facilitate epidemic control through more precise deployment of public health measures aimed at HIV treatment and viral load suppression, which eliminates risk of transmission.This study was prospectively registered on ClinicalTrials.gov, Identifier: NCT04108078, on September 27, 2019.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.056 | 0.007 |
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".