Ngutulu Kagwero (agents of change): study design of a participatory comic pilot study on sexual violence prevention and post-rape clinical care with refugee youth in a humanitarian setting in Uganda
Bibliographic record
Abstract
With over 1.4 million refugees, Uganda is Sub-Saharan Africa's largest refugee-hosting nation. Bidi Bidi, Uganda's largest refugee settlement, hosts over 230,000 residents. There is a dearth of evidence-based sexual violence prevention and post-rape clinical care interventions in low- and middle-income humanitarian contexts tailored for refugee youth. Graphic medicine refers to juxtaposing images and narratives, often through using comics, to convey health promotion messaging. Comics can offer youth-friendly, low-cost, scalable approaches for sexual violence prevention and care. Yet there is limited empirical evaluation of comic interventions for sexual violence prevention and post-rape clinical care. This paper details the study design used to develop and pilot test a participatory comic intervention focused on sexual violence prevention through increasing bystander practices, reducing sexual violence stigma, and increasing post exposure prophylaxis (PEP) knowledge with youth aged 16-24 and healthcare providers in Bidi Bidi. Participants took part in a single-session peer-facilitated workshop that explored social, sexual, and psychological dimensions of sexual violence, bystander interventions, and post-rape clinical care. In the workshop, participants completed a participatory comic book based on narratives from qualitative data conducted with refugee youth sexual violence survivors. This pilot study employed a one-group pre-test/post-test design to assess feasibility outcomes and preliminary evidence of the intervention's efficacy. Challenges included community lockdowns due to COVID-19 which resulted in study implementation delays, political instability, and attrition of participants during follow-up surveys. Lessons learned included the important role of youth facilitation in youth-centred interventions and the promise of participatory comics for youth and healthcare provider engagement for developing solutions and reducing stigma regarding SGBV. The Ngutulu Kagwero (Agents of change) project produced a contextually and age-tailored comic intervention that can be implemented in future fully powered randomized controlled trials to determine effectiveness in advancing sexual violence prevention and care with youth in humanitarian contexts.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".