Stroke virtual rehabilitation in rural communities: exploring the perceptions of stroke survivors, caregivers, clinicians, and health administrators
Bibliographic record
Abstract
PURPOSE: Rural-dwelling stroke survivors have unmet rehabilitation needs after returning to community-living. Virtual rehabilitation, defined as the use of technology to provide rehabilitation services from a distance, could be a viable and timely solution to address this need, especially within the COVID-19 pandemic context. There is still a minimal understanding of virtual rehabilitation delivery within rural contexts. This study sought to explore the perceptions of rural stakeholders about virtual stroke rehabilitation. METHODS: = 3), and analyzed to understand their experiences and perceptions of virtual stroke rehabilitation. RESULTS: We identified three overarching themes from the participant responses (1) The Root of the (Rural) Problem considered how systemic inequities impact stroke survivors' and caregivers' access to stroke recovery services; (2) Common Benefits, Different Challenges identified the unique benefits and challenges of delivering virtual rehabilitation within rural contexts; and (3) Ingredients for Success described important considerations for implementing virtual rehabilitation. CONCLUSION: Virtual rehabilitation is generally accepted by all stakeholders as a supplement to in-person services. Addressing the unique barriers faced by rural clinicians and stroke survivors is necessary to provide successful virtual rehabilitation.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".