A community engagement approach for an integrated early childhood development intervention: a case study of an urban informal settlement with Kenyans and embedded refugees
Bibliographic record
Abstract
BACKGROUND: Community engagement is crucial for the design and implementation of community-based early childhood development (ECD) programmes. This paper aims to share key components and learnings of a community engagement process for an integrated ECD intervention. The lessons shared are drawn from a case study of urban informal settlement with embedded refugees in Nairobi, Kenya. METHODS: We conducted three stakeholder meetings with representatives from the Ministry of Health at County and Sub-County, actors in the ECD sector, and United Nations agency in refugee management, a transect walk across five villages (Ngando, Muslim, Congo, Riruta and Kivumbini); and, six debrief meetings by staff from the implementing organization. The specific steps and key activities undertaken, the challenges faced and benefits accrued from the community engagement process are highlighted drawing from the implementation team's perspective. RESULTS: Context relevant, well-planned community engagement approaches can be integrated into the five broad components of stakeholder engagement, formative research, identification of local resources, integration into local lives, and shared control/leadership with the local community. These can yield meaningful stakeholder buy-in, community support and trust, which are crucial for enabling ECD programme sustainability. CONCLUSION: Our experiences underscore that intervention research on ECD programmes in urban informal settlements requires a well-planned and custom-tailored community engagement model that is sensitive to the needs of each sub-group within the community to avoid unintentionally leaving anyone out.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".