Exploring the experiences of adults with stroke in virtual community-based stroke programs: a qualitative descriptive study
Bibliographic record
Abstract
Abstract Background Stroke is among the top contributors to disability and can impact an individual’s cognition, physical functioning, and mental health. Since the COVID-19 pandemic, several community-based organizations have started delivering stroke programs virtually. However, participants’ experiences in these programs remain understudied, and evidence-based guidelines to inform and optimize virtual stroke program development and delivery are lacking. Thus, this study aimed to describe the perspectives and experiences of individuals with stroke who participated in virtual community-based organization stroke programs, including perceived access and participation facilitators and barriers and suggestions for improving these programs. Methods A qualitative descriptive design was used to gather participant experiences through semi-structured interviews. Audio-recorded interviews were conducted on Zoom and transcribed verbatim. Adult participants who had experienced a stroke and attended at least one Canadian virtual community-based organization stroke program were recruited. Data were analyzed using inductive thematic analysis. Results Twelve participants (32–69 years, 2–23 years post-stroke, eight women and four men) participated in this study. Five themes were identified: (1) motives to join virtual community-based organization stroke programs, including gaining peer connections, knowledge and information; (2) perceived barriers to accessing and participating in virtual community-based organization stroke programs, including technology inequities, difficulties navigating technology, and inadequate facilitation; (3) perceived facilitators to accessing and participating in virtual community-based organization stroke programs, including remote access, virtual platform features and program leader characteristics/skills; (4) unmet needs during virtual community-based organization stroke programs, including in-person connection and individualized support; and (5) suggestions and preferences for improving virtual community-based organization stroke programs, including program facilitation, content and format. Conclusions Study findings highlight opportunities to improve virtual community-based organization stroke programs to optimize participant experiences and outcomes. Addressing the barriers and suggestions identified in this study may improve virtual community-based organization stroke programs’ access and quality.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".