Implementation science in nutrition practice: A review of the Consolidated Framework for Implementation Research
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.
Bibliographic record
Abstract
Many nutrition interventions and innovations are supported by strong evidence; however, their adoption, implementation, and long-term sustainability in real-world healthcare settings too frequently remain a challenge. Implementation science offers methodologies to equip practitioners with tools to identify and address the contextual factors that influence implementation success in health settings (e.g., adoption, implementation, sustainability). Among the various frameworks and theories used in implementation science, the Consolidated Framework of Implementation Research (CFIR) is one of the most widely used. The CFIR synthesizes constructs from multiple behavioral and implementation theories into a comprehensive tool that can be used to systematically assess the barriers and facilitators that influence implementation outcomes. The framework enables practitioners and researchers to identify context-specific implementation determinants and to design tailored implementation strategies across diverse contexts and settings. Given its adaptability, the CFIR is highly relevant to the field of nutrition and dietetics to support sustained adoption and delivery of nutrition innovations (e.g., screening tools, educational programs, quality improvement initiatives); but it is relatively underutilized in nutrition practice. This article provides an overview of the CFIR and illustrates how it can be used to guide the implementation of nutrition innovations in clinical practice through two pragmatic case studies. We highlight the potential of the CFIR to be used as a guiding framework to advance the adoption, implementation, and sustainability of nutrition innovations and improve nutrition care and patient outcomes.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.101 | 0.260 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it