From collaborator to colleague: a community-based program science approach for engaging Kenyan communities of gay, bisexual and other men who have sex with men in HIV research
Bibliographic record
Abstract
Since the 1990s, researchers have used community-based participatory approaches to achieve outcomes relevant to local communities, to build collaborative and sustainable research infrastructures, and to address disparities in knowledge production. Notwithstanding these strengths, communities and researchers have questioned its success in addressing power imbalances inherent in collaborative research encounters. In this methodological paper, we describe a novel community-based program science approach to guide an interdisciplinary research project on HIV self-testing among men who have sex with men in three Kenyan counties. Drawing on ethnographic field notes, we detail how community researchers and their academic and programmatic partners collaborated through all phases of the research process, including research design and data collection. Importantly, community researchers also played an integral role in data analysis and dissemination, going well beyond the conventional role of ‘community engagement’ in global health research. We also present findings from qualitative interviews conducted by community researchers with their peers to inform the rollout of HIV self-testing kits in their respective county-contexts. Our approach highlights that engaging community directly in evidence production allows research findings – owned and generated by communities on their own behalf – to be fed more swiftly and effectively into community-led program delivery.
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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 | 0.059 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.028 | 0.018 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".