Prioritizing research for integrated implementation of early childhood development and maternal, newborn, child and adolescent health and nutrition platforms
Bibliographic record
Abstract
BACKGROUND: Existing health and nutrition services present potential platforms for scaling up delivery of early childhood development (ECD) interventions within sensitive windows across the life course, especially in the first 1000 days from conception to age 2 years. However, there is insufficient knowledge on how to optimize implementation for such strategies in an integrated manner. In light of this knowledge gap, we aimed to systematically identify a set of integrated implementation research priorities for health, nutrition and early child development within the 2015 to 2030 timeframe of the Sustainable Development Goals (SDGs). METHODS: We applied the Child Health and Nutrition Research Initiative method, and consulted a diverse group of global health experts to develop and score 57 research questions against five criteria: answerability, effectiveness, deliverability, impact, and effect on equity. These questions were ranked using a research priority score, and the average expert agreement score was calculated for each question. FINDINGS: The research priority scores ranged from 61.01 to 93.52, with a median of 82.87. The average expert agreement scores ranged from 0.50 to 0.90, with a median of 0.75. The top-ranked research question were: i) "How can interventions and packages to reduce neonatal mortality be expanded to include ECD and stimulation interventions?"; ii) "How does the integration of ECD and MNCAH&N interventions affect human resource requirements and capacity development in resource-poor settings?"; and iii) "How can integrated interventions be tailored to vulnerable refugee and migrant populations to protect against poor ECD and MNCAH&N outcomes?". Most highly-ranked research priorities varied across the life course and highlighted key aspects of scaling up coverage of integrated interventions in resource-limited settings, including: workforce and capacity development, cost-effectiveness and strategies to reduce financial barriers, and quality assessment of programs. CONCLUSIONS: Investing in ECD is critical to achieving several of the SDGs, including SDG 2 on ending all forms of malnutrition, SDG 3 on ensuring health and well-being for all, and SDG 4 on ensuring inclusive and equitable quality education and promotion of life-long learning opportunities for all. The generated research agenda is expected to drive action and investment on priority approaches to integrating ECD interventions within existing health and nutrition services.
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 | 0.458 | 0.410 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".