From community as data providers to data users: developing a community-led research platform using routine program data in Kenya
Bibliographic record
Abstract
Community-based organizations (CBOs) are critical in providing trusted and tailored HIV/STI services to gay, bisexual, and other men who have sex with men (GBMSM). Despite significant strides in CBO involvement in HIV/STI research in Kenya, there remain gaps in meaningful engagement and capacity-building, especially quantitative research. We share our experience and lessons learned in developing HEKA (Health Research Intervention Kuthamini Afya Yetu), a community-led research platform where community members are leveraging their routinely collected program data to design research aimed at strengthening HIV/STI programs. HEKA focuses on building capacity and quantitative scientific literacy within CBOs. Guided by the program science framework, an iterative, bi-directional framework linking research and program implementation, our seven CBOs identified areas for quantitative skills development and together with academic partners, established interactive learning activities through a workshop and set a common research agenda for future steps. The collaborative process centered around applying the skills learned to appraise program coverage and its drivers, so as to improve HIV/STI outcomes for the communities we serve. The workshop included introductory sessions on quantitative research methods, data structures, and R programming (an open-access software environment for data management and analysis). We also maintained engagement through a new online group where we have met monthly. Through our experience, we learned that using a co-leadership framework where research direction evolves through shared/delegated leadership between staff from the different organizations and peer-to-peer mentorship was instrumental to our success. However, we encountered some challenges in the process, including sustainability of funding to maintain engagement. Other challenges have included balancing varied learning paces due to diverse staff roles, navigating a volatile socio-political climate with regard to GBMSM issues, and long commutes for in-person meetings. Competing demands from program funders, such as stringent monthly reporting requirements amongst these, have also contributed to delays in participation. Despite these challenges, HEKA demonstrates the potential for community-based and led research in the HIV/STI field. Our experience can serve as a model for other CBOs aiming to lead collaborative or independent research and build capacity.
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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 | 0.086 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".