Using the knowledge to action process model to incite clinical change *
Bibliographic record
Abstract
INTRODUCTION: Knowledge translation (KT) has only recently emerged in the field of rehabilitation with attention on creating effective KT interventions to increase clinicians' knowledge and use of evidence-based practice (EBP). The uptake of EBP is a complex process that can be facilitated by the use of the Knowledge to Action Process model. This model provides a sequence of phases for researchers and clinicians to follow in order to optimize KT across various fields of practice. METHODS: In this article we use an example from a series of national studies in stroke rehabilitation to demonstrate how the Knowledge to Action Process model is being used to increase the use of best practices in the management of a very prevalent poststroke impairment, unilateral spatial neglect. RESULTS: The series of research projects and actions described herein each address a specific phase of the model. The reader is introduced to a specific example with the goal of generalizing the process to his or her own domain of interest. Gaps in our research agenda are also highlighted and future initiatives to complete the process are described. DISCUSSION: It is important that KT is maximized in health care to improve patient outcomes. As demonstrated here, the Knowledge to Action Process model provides an excellent guide for clinicians, managers, and researchers who wish to incite change in patient 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 | 0.044 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".