Harnessing the power of the grassroots to conduct public health research in sub-Saharan Africa: a case study from western Kenya in the adaptation of community-based participatory research (CBPR) approaches
Bibliographic record
Abstract
BACKGROUND: Community-based participatory research (CBPR) is a collaborative approach to research that involves the equitable participation of those affected by an issue. As the field of global public health grows, the potential of CBPR to build capacity and to engage communities in identification of problems and development and implementation of solutions in sub-Saharan Africa has yet to be fully tapped. The Orphaned and Separated Children's Assessments Related to their Health and Well-Being (OSCAR) project is a longitudinal cohort of orphaned and non-orphaned children in Kenya. This paper will describe how CBPR approaches and principles can be incorporated and adapted into the study design and methods of a longitudinal epidemiological study in sub-Saharan Africa using this project as an example. METHODS: The CBPR framework we used involves problem identification, feasibility and planning; implementation; and evaluation and dissemination. This case study will describe how we have engaged the community and adapted CBPR methods to OSCAR's Health and Well-being Project's corresponding to this framework in four phases: 1) community engagement, 2) sampling and recruitment, 3) retention, validation, and follow-up, and 4) analysis, interpretation and dissemination. RESULTS: To date the study has enrolled 3130 orphaned and separated children, including children living in institutional environments, those living in extended family or other households in the community, and street-involved children and youth. Community engagement and participation was integral in refining the study design and identifying research questions that were impacting the community. Through the participation of village Chiefs and elders we were able to successfully identify eligible households and randomize the selection of participants. The on-going contribution of the community in the research process has been vital to participant retention and data validation while ensuring cultural and community relevance and equity in the research agenda. CONCLUSION: CBPR methods have the ability to enable and strengthen epidemiological and public health research in sub-Saharan Africa within the social, political, economic and cultural contexts of the diverse communities on the continent. This project demonstrates that adaptation of these methods is crucial to the successful implementation of a community-based project involving a highly vulnerable population.
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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 | 0.025 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| 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".