Evaluating the Dissemination and Implementation Impact of a Rehabilitation Intervention: The Graded Repetitive Arm Supplementary Program (GRASP)
Bibliographic record
Abstract
Purpose: To evaluate the dissemination and implementation impacts of a rehabilitation intervention. Methods: Systematic evaluation of data sources including academic publishing metrics, publications, and surveys was used to describe the dissemination and implementation impact of the graded repetitive arm supplementary program (GRASP). Three categories in the Payback Framework were evaluated: knowledge production and dissemination, benefits to future research and research use, and real-world uptake and implementation. Results: In the Knowledge production and dissemination category, seven publications, authored by the GRASP research team, were associated with the GRASP, and there were approximately 17,000 download counts of GRASP manuals from the website from 120 countries. In the Benefits to future research and research use category, 15 studies and 8 registered clinical trials, authored by researchers outside of the GRASP team, have used GRASP as an intervention. In the real-world uptake and implementation category, GRASP has informed recommendations in 2 clinical guidelines and 20 review papers, and had high implementation uptake (e.g., 35% [53/154] of UK therapists surveyed had used GRASP; 95% [649/681] who downloaded GRASP had used it). More than 75% of those who had used GRASP identified that GRASP provides more intensity in upper extremity rehabilitation, is evidence-based and easy to implement, and the equipment and manual are easy to obtain. Conclusion: The Payback Framework is useful to evaluate the dissemination and implementation impacts of a rehabilitation intervention. GRASP has been implemented extensively in clinical practice and community in a relatively short time since it has been developed.
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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 | 0.266 | 0.495 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".