Development of a theory of implementation and integration: Normalization Process Theory
Bibliographic record
Abstract
BACKGROUND: Theories are important tools in the social and natural sciences. The methods by which they are derived are rarely described and discussed. Normalization Process Theory explains how new technologies, ways of acting, and ways of working become routinely embedded in everyday practice, and has applications in the study of implementation processes. This paper describes the process by which it was built. METHODS: Between 1998 and 2008, we developed a theory. We derived a set of empirical generalizations from analysis of data collected in qualitative studies of healthcare work and organization. We developed an applied theoretical model through analysis of empirical generalizations. Finally, we built a formal theory through a process of extension and implication analysis of the applied theoretical model. RESULTS: Each phase of theory development showed that the constructs of the theory did not conflict with each other, had explanatory power, and possessed sufficient robustness for formal testing. As the theory developed, its scope expanded from a set of observed regularities in data with procedural explanations, to an applied theoretical model, to a formal middle-range theory. CONCLUSION: Normalization Process Theory has been developed through procedures that were properly sceptical and critical, and which were opened to review at each stage of development. The theory has been shown to merit formal testing.
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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 | 0.043 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".