Implementation in rehabilitation: a roadmap for practitioners and researchers
Bibliographic record
Abstract
Purpose: Despite growth in rehabilitation research, implementing research findings into rehabilitation practice has been slow. This creates inequities for patients and is an ethical issue. However, methods to investigate and facilitate evidence implementation are being developed. This paper aims to make these methods relevant and accessible for rehabilitation researchers and practitioners.Methods: Rehabilitation practice is varied and complex and occurs within multilevel healthcare systems. Using a “road map” analogy, we describe how implementation concepts and theories can inform implementation strategies in rehabilitation. The roadmap involves a staged journey that considers: the nature of evidence; context for implementation; navigation tools for implementation; strategies to facilitate implementation; evaluation of implementation outcomes; and sustainability of implementation. We have developed a model to illustrate the journey, and four case studies exemplify implementation stages in rehabilitation settings.Results and Conclusions: Effective implementation strategies for the complex world of rehabilitation are urgently required. The journey we describe unpacks that complexity to provide a template for effective implementation, to facilitate translation of the growing evidence base in rehabilitation into improved patient outcomes. It emphasizes the importance of understanding context and application of relevant theory, and highlights areas which should be targeted in new implementation research in rehabilitation.Implications for rehabilitationEffective implementation of research evidence into rehabilitation practice has many interconnected steps and a roadmap analogy is helpful in defining them.Understanding context for implementation is critically important and using theory can facilitate development of understanding.Research methods for implementation in rehabilitation should be carefully selected and outcomes should evaluate implementation success as well as clinical change.Sustainability requires regular revisiting of the interconnected steps.
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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 | 0.282 | 0.211 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.019 | 0.034 |
| Scholarly communication | 0.052 | 0.095 |
| Open science | 0.013 | 0.040 |
| Research integrity | 0.046 | 0.049 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".