Contextual design choices and partnerships for scaling early child development programmes
Bibliographic record
Abstract
to implement ECD programmes at national and regional scale. We completed a literature review and participatory mixed-method evaluation of projects in Saving Brains®, Grand Challenges Canada® funded ECD portfolio across 23 low- and middle-income countries (LMIC). Using an adapted programme cycle, findings from evaluation related to partnerships and leadership, situational analyses, and design for scaling ECD were considered. 39 projects (5 'Transition to Scale' and 34 'Seed') were evaluated. 63% were delivered through health and 84% focused on Responsive Caregiving and Early Learning (RCEL). Multilevel partnerships, leadership and targeted situational analysis were crucial to design and adaptation. A theory of change approach to consider pathways to impact was useful for design, but practical situational analysis tools and local data to guide these processes were lacking. Several RCEL programmes, implemented within government services, had positive impacts on ECD outcomes and created more enabling caregiving environments. Engagement of informal and private sectors provided an alternative approach for reaching children where government services were sparse. Cost-effectiveness was infrequently measured. At small-scale RCEL interventions can be successfully adapted and implemented across diverse settings through processes which are responsive to situational analysis within a partnership model. Accelerating progress will require longitudinal evaluation of ECD interventions at much larger scale, including programmes targeting children with disabilities and humanitarian settings with further exploration of cost-effectiveness, critical content and human resources.
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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 | 0.097 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".