Closing the know-do gap for child health: UNICEF’s experiences from embedding implementation research in child health and nutrition programming
Bibliographic record
Abstract
UNICEF operates in 190 countries and territories, where it advocates for the protection of children's rights and helps meet children's basic needs to reach their full potential. Embedded implementation research (IR) is an approach to health systems strengthening in which (a) generation and use of research is led by decision-makers and implementers; (b) local context, priorities, and system complexity are taken into account; and (c) research is an integrated and systematic part of decision-making and implementation. By addressing research questions of direct relevance to programs, embedded IR increases the likelihood of evidence-informed policies and programs, with the ultimate goal of improving child health and nutrition.This paper presents UNICEF's embedded IR approach, describes its application to challenges and lessons learned, and considers implications for future work.From 2015, UNICEF has collaborated with global development partners (e.g. WHO, USAID), governments and research institutions to conduct embedded IR studies in over 25 high burden countries. These studies focused on a variety of programs, including immunization, prevention of mother-to-child transmission of HIV, birth registration, nutrition, and newborn and child health services in emergency settings. The studies also used a variety of methods, including quantitative, qualitative and mixed-methods.UNICEF has found that this systematically embedding research in programs to identify implementation barriers can address concerns of implementers in country programs and support action to improve implementation. In addition, it can be used to test innovations, in particular applicability of approaches for introduction and scaling of programs across different contexts (e.g., geographic, political, physical environment, social, economic, etc.). UNICEF aims to generate evidence as to what implementation strategies will lead to more effective programs and better outcomes for children, accounting for local context and complexity, and as prioritized by local service providers. The adaptation of implementation research theory and practice within a large, multi-sectoral program has shown positive results in UNICEF-supported programs for children and taking them to scale.
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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 | 0.221 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.026 | 0.029 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.005 | 0.041 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 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".