Monitoring, evaluation and accountability evidence use for design, adaptation, and scale-up of an early childhood development program in Rwanda
Bibliographic record
Abstract
Introduction: The first three years of a child's life are the most critical to child development and have an impact on the future achievement of the child. Young children's healthy development depends on nurturing care that ensures health, nutrition, responsive caregiving, safety, and security. Parents & other adult caregivers play a critical role in moderating children's early experiences, which has a lasting impact be it positive or negative on the children's future. Parenting education programs are proven to improve parental skills, capacity, and efficacy in a way that supports improved child development outcomes. Yet, most parents in low-middle-income countries such as Rwanda lack access to information and skills on how to support their children's holistic development. In response, Save The Children implemented the First Steps "Intera za Mbere" holistic parenting education project in Rwanda from 2014 to 2021. This paper reflects on how monitoring, evaluation, accountability, and learning (MEAL) approaches were applied throughout the project cycle and their impact on program improvement and national policy and advocacy. This paper explores how the aspirations for measurement for change, considerations for innovation uptake and frameworks for learning about improvement are reflected in this project. Methods: The project utilized qualitative and quantitative MEAL across the program cycle. Action research at the start of the project identified promoters and inhibitors of high-quality nurturing care and program delivery modalities. The project utilized a randomized control trial to provide insight into components that work better for parenting education. Evidence from surveys done remotely via phones was used to inform COVID-19 adaptations of the program. Results: The application of MEAL evidence led to the successful development and improvement of the program. At the policy level, evidence from the project influenced the review of the 2016 National Integrated ECD policy and the development of the national parenting education framework. Conclusion: The regular use of evidence from MEAL is critical for program improvement, scale-up, and policy influence.
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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 | 0.263 | 0.369 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".