‘This is what is going to help me’: Developing a co-designed and theoretically informed harm reduction intervention for mobile youth in South Africa and Uganda
Bibliographic record
Abstract
Young migrants in sub-Saharan Africa are particularly vulnerable to HIV-acquisition. Despite this, they are consistently under-served by services, with low uptake and engagement. We adopted a community-based participatory research approach to conduct longitudinal qualitative research among 78 young migrants in South Africa and Uganda. Using repeat in-depth interviews and participatory workshops we sought to identify their specific support needs, and to collaboratively design an intervention appropriate for delivery in their local contexts. Applying a protection-risk conceptual framework, we developed a harm reduction intervention which aims to foster protective factors, and thereby nurture resilience, for youth 'on the move' within high-risk settings. Specifically, by establishing peer supporter networks, offering a 'drop-in' resource centre, and by identifying local adult champions to enable a supportive local environment. Creating this supportive edifice, through an accessible and cohesive peer support network underpinned by effective training, supervision and remuneration, was considered pivotal to nurture solidarity and potentially resilience. This practical example offers insights into how researchers may facilitate the co-design of acceptable, sustainable interventions.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".