Case Series of a Knowledge Translation Intervention to Increase Upper Limb Exercise in Stroke Rehabilitation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Current approaches to upper limb rehabilitation are not sufficient to drive neural reorganization and maximize recovery after stroke. To address this evidence-practice gap, a knowledge translation intervention using the Behaviour Change Wheel was developed. The intervention involves collaboratively working with stroke therapy teams to change their practice and increase therapy intensity by therapists prescribing supplementary self-directed arm exercise. The purposes of this case series are: (1) to provide an illustrative example of how a research-informed process changed clinical practice and (2) to report on staff members' and patients' perceptions of the utility of the developed intervention. CASE DESCRIPTIONS: A participatory action research approach was used in 3 stroke rehabilitation units in the United Kingdom. The intervention aimed to change 4 therapist-level behaviors: (1) screening patients for suitability for supplementary self-directed arm exercise, (2) provision of exercises, (3) involving family and caregivers in assisting with exercises, and (4) monitoring and progressing exercises. Data on changes in practice were collected by therapy teams using a bespoke audit tool. Utility of the intervention was explored in qualitative interviews with patients and staff. OUTCOMES: Components of the intervention were successfully embedded in 2 of the 3 stroke units. At these sites, almost all admitted patients were screened for suitability for supplementary self-directed exercise. Exercises were provided to 77%, 70%, and 88% of suitable patients across the 3 sites. Involving family and caregivers and monitoring and progressing exercises were not performed consistently. CONCLUSIONS: This case series is an example of how a rigorous research-informed knowledge translation process resulted in practice change. Research is needed to demonstrate that these changes can translate into increased intensity of upper limb exercise and affect patient outcomes.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".