Testing the <scp>C</scp>onsolidated <scp>F</scp>ramework for <scp>I</scp>mplementation <scp>R</scp>esearch on health care innovations from <scp>S</scp>outh <scp>Y</scp>orkshire
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: There is an international imperative to implement research into clinical practice to improve health care. Understanding the dynamics of change requires knowledge from theoretical and empirical studies. This paper presents a novel approach to testing a new meta theoretical framework: the Consolidated Framework for Implementation Research. METHOD: The utility of the Framework was evaluated using a post hoc, deductive analysis of 11 narrative accounts of innovation in health care services and practice from England, collected in 2010. A matrix, comprising the five domains and 39 constructs of the Framework was developed to examine the coherence of the terminology, to compare results across contexts and to identify new theoretical developments. RESULTS: The Framework captured the complexity of implementation across 11 diverse examples, offering theoretically informed, comprehensive coverage. The Framework drew attention to relevant points in individual cases together with patterns across cases; for example, all were internally developed innovations that brought direct or indirect patient advantage. In 10 cases, the change was led by clinicians. Most initiatives had been maintained for several years and there was evidence of spread in six examples. Areas for further development within the Framework include sustainability and patient/public engagement in implementation. CONCLUSION: Our analysis suggests that this conceptual framework has the potential to offer useful insights, whether as part of a situational analysis or by developing context-specific propositions for hypothesis testing. Such studies are vital now that innovation is being promoted as core business for health care.
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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 | 0.149 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".