The role of theories and models in implementation science: An example of application in neurorehabilitation / Die Rolle von Theorien und Modellen in der Implementierungsforschung: ein Anwendungsbeispiel aus der Neurorehabilitation
Bibliographic record
Abstract
Abstract Implementation science investigates how scientific knowledge can be effectively and sustainably translated into practice. A variety of models, theories and frameworks provide structured and theoretically grounded approaches for planning, executing and evaluating implementation efforts. The need for theory-driven implementation research is also evident in health professions within German-speaking countries. Within physical therapy in Germany, particularly in the field of neurological rehabilitation, gaps between theoretical knowledge and practical applications in routine care have been identified; for example, the limited use of standardized assessments. Despite recommendations from national and international guidelines, assessments are still under-utilized in neurological physical therapy. Implementation science, therefore, may play a critical role in bridging the knowledge-to-practice gap in physical therapy in Germany. This article describes the design of the research project AssessMobility, a multi-center, multi-method implementation study with eleven sites within a healthcare organization using the physical therapy profession as an example. The purpose of AssessMobility is the development, implementation and evaluation of a knowledge transfer intervention aimed at integrating standardized assessments for measuring balance and mobility into routine care of neurological departments in a cross-setting approach. AssessMobility is conceptualized as a collaborative project with clinical partners and based on the Knowledge-to-Action (KTA) cycle, an established process model for translating knowledge into practice. Additional models, theories and frameworks, such as the Theoretical Domains Framework, are applied within the KTA cycle. We outline the project phases using the KTA cycle and provide methodological considerations for the application of specific models, theories, and frameworks.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".