A formative evaluation of the implementation of an upper limb stroke rehabilitation intervention in clinical practice: a qualitative interview study
Bibliographic record
Abstract
BACKGROUND: The Graded Repetitive Arm Supplementary Program (GRASP) is a hand and arm exercise programme designed to increase the intensity of exercise achieved in inpatient stroke rehabilitation. GRASP was shown to be effective in a randomised controlled trial in 2009 and has since experienced unusually rapid uptake into clinical practice. The aim of this study was to conduct a formative evaluation of the implementation of GRASP to inform the development and implementation of a similar intervention in the United Kingdom. METHODS: Semi-structured interviews were conducted with therapists who were involved in implementing GRASP at their work site, or who had experience of using GRASP. Normalisation Process Theory (NPT), a sociological theory used to explore the processes of embedding innovations in practice, was used to develop an interview guide. Intervention components outlined within the GRASP Guideline Manual were used to develop prompts to explore how therapists use GRASP in practice. Interview transcripts were analysed using a coding frame based on implementation theory. RESULTS: Twenty interviews were conducted across eight sites in British Columbia Canada. Therapists identified informal networks and the free online availability of GRASP as key factors in finding out about the intervention. All therapists reported positive opinions about the value of GRASP. At all sites, therapists identified individuals who advocated for the use of GRASP, and in six of the eight sites this was the practice leader or senior therapist. Rehabilitation assistants were identified as instrumental in delivering GRASP in almost all sites as they were responsible for organising the GRASP equipment and assisting patients using GRASP. Almost all intervention components were found to be adapted to some degree when used in clinical practice; coverage was wider, the content adapted, and the dose, when monitored, was less. CONCLUSIONS: Although GRASP has translated into clinical practice, it is not always used in the way in which it was shown to be effective. This formative evaluation has informed the development of a novel intervention which aims to bridge this evidence-practice gap in upper limb rehabilitation after stroke.
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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 | 0.102 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".