Technology Use for Home-Based Stroke Rehabilitation in Switzerland From the Perspectives of Persons Living With Stroke, Informal Caregivers, and Therapists: Qualitative Interview and Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Stroke is a leading cause for long-term disability, requiring both inpatient and outpatient rehabilitation and self-training in the home environment. Technology-based tools are gradually gaining acceptance as additional and suitable options for extending the rehabilitation process. While the experiences of persons living with stroke, therapists, and informal caregivers with respect to technology use have already been investigated in other countries, this topic is underexplored in the Swiss context. OBJECTIVE: We aimed to explore the experiences and needs of persons living with stroke, informal caregivers, and therapists in using technology-based tools in a home environment for stroke rehabilitation in Switzerland. METHODS: This study followed a qualitative descriptive methodology, including semistructured interviews and focus group discussions. We applied a deductive template analysis alongside the accessibility, adaptability, accountability, and engagement framework to analyze the qualitative data sets for technology-assisted solutions for poststroke rehabilitation. RESULTS: We collected the experiences and needs of persons living with stroke (7/23, 30%), informal caregivers (4/23, 17%), and therapists (occupational and physical therapists; 12/23, 52%). The 4 categories we used to organize the analysis and results were accessibility to quality rehabilitation, adaptability to patient differences, accountability or compliance with rehabilitation, and engagement with rehabilitation. Persons living with stroke stated that they use various tools within their rehabilitation process depending on their specific needs. They felt that there is a plethora of tools available but sometimes felt overwhelmed with the selection process. Informal caregivers indicated that they generally felt underserved and insufficiently informed throughout the rehabilitation process. They reported that they use technology-based tools to support their relatives affected by stroke in becoming more independent. Therapists appreciate the numerous possible applications of technology-based tools in rehabilitation. At the same time, however, they express dissatisfaction with the lack of clarity in Switzerland regarding cost coverage, recommendations, and training opportunities. CONCLUSIONS: Persons living with stroke, informal caregivers, and therapists in Switzerland reported varied and unique experiences and needs with the use of technology-based tools in outpatient stroke rehabilitation. Written recommendations, the assumption of financial costs, and the provision of information and education could foster increased confidence in the use of technology-based tools for patients and therapists.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".