Implementation Science in Nutrition: Concepts and Frameworks for an Emerging Field of Science and Practice
Bibliographic record
Abstract
Malnutrition in all its forms has risen on global and national agendas in recent years because of the recognition of its magnitude and its consequences for a wide range of human, social, and economic outcomes. Although the WHO, national governments, and other organizations have endorsed targets and identified appropriate policies, programs, and interventions, a major challenge lies in implementing these with the scale and quality needed to achieve population impact. This paper presents an approach to implementation science in nutrition (ISN) that builds upon concepts developed in other policy domains and addresses critical gaps in linking knowledge to effective action. ISN is defined here as an interdisciplinary body of theory, knowledge, frameworks, tools, and approaches whose purpose is to strengthen implementation quality and impact. It includes a wide range of methods and approaches to identify and address implementation bottlenecks; means to identify, evaluate, and scale up implementation innovations; and strategies to enhance the utilization of existing knowledge, tools, and frameworks based on the evolving science of implementation. The ISN framework recognizes that quality implementation requires alignment across 5 domains: the intervention, policy, or innovation being implemented; the implementing organization(s); the enabling environment of policies and stakeholders; the individuals, households, and communities of interest; and the strategies and decision processes used at various stages of the implementation process. The success of aligning these domains through implementation research requires a culture of inquiry, evaluation, learning, and response among program implementers; an action-oriented mission among the research partners; continuity of funding for implementation research; and resolving inherent tensions between program implementation and research. The Society for Implementation Science in Nutrition is a recently established membership society to advance the science and practice of nutrition implementation at various scales and in varied contexts.
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 | 0.282 | 0.220 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.010 | 0.116 |
| Scholarly communication | 0.037 | 0.032 |
| Open science | 0.009 | 0.023 |
| Research integrity | 0.017 | 0.026 |
| 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".