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Record W2897684703 · doi:10.1093/cdn/nzy080

Implementation Science in Nutrition: Concepts and Frameworks for an Emerging Field of Science and Practice

2018· article· en· W2897684703 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Developments in Nutrition · 2018
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsNutrition International
Fundersnot available
KeywordsProcess (computing)Implementation researchQuality (philosophy)Scale (ratio)Knowledge managementManagement scienceComputer scienceField (mathematics)Action (physics)Process managementPsychological interventionBusinessEngineeringMedicine

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.470
Teacher spread0.413 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it