Scaling Up Whole Child Development Initiatives: Lessons from the Early Journey of Life Program in Vietnam
Bibliographic record
Abstract
Abstract Human development programs—for example, early childhood interventions—often depend on high-quality, respectful human relationships. This creates a challenge for implementation at large scale. Here, we review how this challenge was addressed in the scale-up of the Early Journey of Life (EJOL) program, an evidence-based initiative in Vietnam that supports parents and families in the first 1,000 days of children’s lives. The program’s experience highlights the value of an approach to scaling that aims to energize stakeholders, including at the front line as well as among government leaders; rapidly learn and adapt based on new evidence; empower leadership across the delivery system; rebalance agency to the front line; and gradually but systematically embed program principles and activities in large government systems. Our findings suggest that EJOL’s work to implement scale-up along these lines was in turn grounded in high-quality relationships with those engaged in the program, from family members to frontline workers to middle managers and senior government officials. The EJOL program has successfully scaled to reach 109 rural communes in Ha Nam province and continues to expand and diversify while retaining its core focus on high-quality human relationships.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".