Delivering Intensive Rehabilitation in Stroke: Factors Influencing Implementation
Bibliographic record
Abstract
Background: The evidence base for stroke rehabilitation recommends intensive and repetitive task-specific practice, as well as aerobic exercise. However, translating these -evidence-based interventions from research into clinical practice remains a major -challenge. Objective: The objective of this study was to investigate factors influencing implementation of higher-intensity activity in stroke rehabilitation settings. Design: This qualitative study used a cross-sectional design. Methods: Semi-structured interviews were conducted with rehabilitation therapists from 4 sites across 2 Canadian provinces who had experience in delivering a higher-intensity intervention as part of a clinical trial (Determining Optimal post-Stroke Exercise [DOSE]). An interview guide was developed, and data were analyzed using implementation frameworks. Results: Fifteen therapists were interviewed before data saturation was reached. Therapists and patients generally had positive experiences regarding high-intensity interventions. However, therapists felt they would adapt the protocol to accommodate their beliefs about ensuring movement quality. The requirement for all patients to have a graded exercise test and the use of sensors (eg, heart rate monitors) gave therapists confidence to push patients harder than they normally would. Paradoxically, a system that enables routine graded exercise testing and the availability of staff and equipment contribute challenges for implementation in everyday practice. Conclusions: Even therapists involved in delivering a high-intensity intervention as part of a trial wanted to adapt it for clinical practice; therefore, it is imperative that researchers are explicit regarding key intervention components and what can be adapted to help ensure implementation fidelity. Changes in therapists' beliefs and system-level changes (staffing and resources) are likely necessary to facilitate higher-intensity rehabilitation in practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".