What implementation evidence matters: scaling‐up nurturing interventions that promote early childhood development
Bibliographic record
Abstract
Research in early childhood development (ECD) has established the need for scaling-up multisectoral interventions for nurturing care to promote ECD, for improved socioeconomic outcomes for sustainable societies. However, key elements and processes for implementation and scale-up of such interventions are not well understood. This special series on implementation research and practice for ECD brings together evidence to inform effectiveness, quality, and scale in nurturing care programs; identifies knowledge gaps; and proposes further directions for research and practice. This paper frames the dimensions and components fundamental to the understanding of implementation processes for nurturing care interventions, factors for improving implementation of interventions, and strategies to scale by embedding interventions in delivery systems. We discuss emerging issues in implementation research for ECD, including (1) the role of context in adaptation and implementation, (2) standardized reporting of implementation research, (3) the importance of feasibility studies to inform scale-up and capacity building, (4) fidelity and program quality improvement, and (5) intervention integration into existing systems. Effective implementation of nurturing care interventions is at the heart of achieving positive developmental outcomes for young children. It is pivotal to adapt and implement these interventions based on evidence for high impact, especially in low-resource settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.361 | 0.704 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".