Implementation and Evaluation of the Virtual Graded Repetitive Arm Supplementary Program (GRASP) for Individuals With Stroke During the COVID-19 Pandemic and Beyond
Bibliographic record
Abstract
OBJECTIVE: Given the uncertainty of the coronavirus disease 2019 (COVID-19) pandemic, implementing telerehabilitation that enables the remote delivery of rehabilitation services is needed to mitigate the spread of COVID-19. We studied the implementation and the effectiveness of the virtual Graded Repetitive Arm Supplementary Program (GRASP) delivered and evaluated via videoconferencing in individuals with stroke. METHODS: The Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework with mixed methods was used to evaluate the implementation of the 2 iterations of the program delivered by a nonprofit organization during the pandemic. RESULTS: Reach: Seventeen people were screened, 13 people were eligible, and 11 consented to participate in the study. Effectiveness: Between baseline and posttest, participants with stroke demonstrated significant improvement in upper extremity function (Arm Capacity and Movement Test) and self-perceived upper extremity (UE) function (Stroke Impact Scale). Adoption: Factors that facilitate program uptake by the staff were well-planned implementation, appropriate screening procedure, and helpful feedback from the audits. All staff felt comfortable using videoconferencing technology to deliver the program despite some technical difficulties. Factors contributing to ongoing participation included that the participants liked the group, they perceived improvements, and the instructor was encouraging. Only one participant with stroke was not comfortable using the videoconferencing technology. Implementation: The program was implemented as intended as evaluated by a fidelity checklist. Participants' adherence was high, as verified by the average attendance and practice time. Maintenance: The organization continued to offer the program. CONCLUSION: The virtual GRASP program was successfully implemented. Although the program was effective in improving both measured and perceived UE function in a small sample of individuals with stroke, caution should be taken in generalizing the results. IMPACT: Implementing telerehabilitation is crucial to optimize patient outcomes and reduce the spread of COVID-19. Our findings provide guidance on the process of delivering a UE rehabilitation program remotely via videoconferencing for stroke. Moreover, insights that arise from this study also inform the implementation of other telerehabilitation services.
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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".