Reducing HIV-related stigma among young people attending school in Northern Uganda: study protocol for a participatory arts-based population health intervention and stepped-wedge cluster-randomized trial
Bibliographic record
Abstract
BACKGROUND: HIV-related stigma negatively impacts HIV prevention, treatment, and care, particularly among children and adolescents in sub-Saharan Africa. Interventions that are culturally grounded and relevant for addressing root causes may reduce the stigma experienced by HIV-positive and HIV-affected young people. This study, to be conducted in a post-conflict, rural setting in Omoro District, Uganda, will develop and evaluate a transformative arts-based HIV-related stigma intervention rooted in local cultural knowledge to reduce stigma and improve HIV prevention and care for young people living with HIV. The intervention will be delivered to young people attending school by community Elders, with the support of teachers, through the transfer of local cultural knowledge and practices with the aim of re-establishing the important cultural and social role of Elders within a community that has suffered the loss of intergenerational transfer of cultural knowledge throughout a 25-year civil war. METHODS: A formative research phase consisting of interviews with students, teachers, and Elders will inform the intervention and provide data for study objectives. Workshops will be delivered to Elders and teachers in participating schools to build capacity for arts-based, educational workshops to be conducted with students in the classroom. The intervention will be evaluated using a stepped-wedge cluster-randomized trial. Government-funded schools in Omoro District will be randomized into three blocks, each comprised of two primary and two secondary schools (n=1800 students). Schools will be randomly assigned to a crossover sequence from control to intervention condition in 8-week intervals. A process evaluation will be implemented throughout the study to evaluate pathways between intervention development, implementation, and effects. DISCUSSION: This study will generate comprehensive, in-depth participatory research and evaluation data to inform an effective and sustainable protocol for implementing arts-based HIV stigma interventions for young people in school settings. Findings will have widespread implications in post-conflict settings for HIV prevention, treatment, and care. TRIAL REGISTRATION: ClinicalTrials.gov NCT04946071 . Registered on 30 June 2021.
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 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".