Community engagement approaches and lessons learned: a case study of the PRECISE pregnancy cohort study in Kenya
Bibliographic record
Abstract
Community engagement (CE) has been recommended as an important ethical consideration for health research to enhance informed consent and exchange knowledge between researchers and community members. The purpose of this paper is to describe how CE was developed and delivered for the PRECISE prospective pregnancy cohort study in Kenya. PRECISE enrolled pregnant women in antenatal care, followed them up to the postpartum period, and collected data and biological samples to enable the study of placental disorders in sub-Saharan Africa. Initially CE was aimed at informing the community about the study, establishing community-wide acceptance of the research and addressing concerns about biological sample collection to facilitate participation in the study. CE later evolved to be a platform for mutual learning aiming to deepen the community's understanding of research principles and informed consent and providing a feedback loop to researchers. We engaged diverse stakeholders including health workers and managers, local administrators, religious and traditional leaders, older women, pregnant women, non-pregnant women and men. We utilized a variety of CE approaches and tools adapting to the specific contextual factors at the study sites. Achievements included widespread understanding of informed consent and research principles, clarification of misconceptions, and dispelling of fears regarding biological sample collection. The relationship with the community was strengthened evidenced by frequent inquiries and active participation in CE activities and the research study. For effective CE, we recommend involvement of community members in the CE team and continuous and adaptive CE throughout the study period.
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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 | 0.039 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".