Understanding implementation context and social processes through integrating Normalization Process Theory (NPT) and the Consolidated Framework for Implementation Research (CFIR)
Bibliographic record
Abstract
BACKGROUND: For successful implementation of an innovation within a complex adaptive system, we need to understand the ways that implementation processes and their contexts shape each other. To do this, we need to explore the work people do to make sense of an innovation and integrate it into their workflow and the contextual elements that impact implementation. Combining Normalization Process Theory (NPT) with the Consolidated Framework for Implementation Research (CFIR) offers an approach to achieve this. NPT is an implementation process theory that explains how changes in the way people think about and use an innovation occurs, while CFIR is a framework that categorizes and describes contextual determinants across five domains that influence implementation. We demonstrate through a case example from our prior research how we integrated NPT and CFIR to inform the development of the interview guide, coding manual, and analysis of the findings. METHODS: In collaboration with our stakeholders, we selected NPT and CFIR to study the implementation process and co-developed an interview guide to elicit responses that would illuminate concepts from both. We conducted, audio-recorded, and transcribed 28 interviews with various professionals involved with the implementation. Based on independent coding of select transcripts and team discussion comparing, clarifying, and crystallizing codes, we developed a coding manual integrating CFIR and NPT constructs. We applied the integrated codes to all interview transcripts. RESULTS: Our findings highlight how integrating CFIR domains with NPT mechanisms adds explanatory strength to the analysis of implementation processes, with particular implications for practical strategies to facilitate implementation. Multiple coding across both theoretical frames captured the entanglement of process and context. Integrating NPT and CFIR enriched understandings of how interactions between implementation processes and contextual determinants shaped each other during implementation. CONCLUSION: The integration of NPT and CFIR provides guidance to identify and explore complex entangled interactions between agents, processes, and contextual conditions within and beyond organizations to embed innovations into routine practices. Nuanced understandings gained through this approach moves understandings beyond descriptions of determinants to explain how change occurs or not during implementation. Mechanism-based explanations illuminate concrete practical strategies to support implementation.
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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 | 0.223 | 0.239 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.023 | 0.037 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".