Challenges and opportunities in coproduction: reflections on working with young people to develop an intervention to prevent violence in informal settlements in South Africa
Bibliographic record
Abstract
Coproduction is widely recognised as essential to the development of effective and sustainable complex health interventions. Through involving potential end users in the design of interventions, coproduction provides a means of challenging power relations and ensuring the intervention being implemented accurately reflects lived experiences. Yet, how do we ensure that coproduction delivers on this promise? What methods or techniques can we use to challenge power relations and ensure interventions are both more effective and sustainable in the longer term? To answer these questions, we openly reflect on the coproduction process used as part of Siyaphambili Youth (‘Youth Moving Forward’), a 3-year project to create an intervention to address the social contextual factors that create syndemics of health risks for young people living in informal settlements in KwaZulu-Natal province in South Africa. We identify four methods or techniques that may help improve the methodological practice of coproduction: (1) building trust through small group work with similar individuals, opportunities for distance from the research topic and mutual exchanges about lived experiences; (2) strengthening research capacity by involving end users in the interpretation of data and explaining research concepts in a way that is meaningful to them; (3) embracing conflicts that arise between researchers’ perspectives and those of people with lived experiences; and (4) challenging research epistemologies through creating spaces for constant reflection by the research team. These methods are not a magic chalice of codeveloping complex health interventions, but rather an invitation for a wider conversation that moves beyond a set of principles to interrogate what works in coproduction practice. In order to move the conversation forward, we suggest that coproduction needs to be seen as its own complex intervention, with research teams as potential beneficiaries.
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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 | 0.051 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.052 | 0.047 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".