Stroke survivor and caregiver perspectives in the development of a community water-based therapeutic exercise program
Bibliographic record
Abstract
BACKGROUND: Water-based therapeutic exercise (WBTE) is an effective approach for stroke survivors to regain strength, mobility, and quality of life. However, existing trials have not included stroke survivors' perspectives in intervention development, which are critical to ensure a WBTE program meets the needs of stroke survivors and provides supports to enable engagement in ongoing exercise. METHODS: Qualitative semi-structured interviews were completed with stroke survivors and caregivers with the aim to have them (1) provide feedback on elements of the intervention resulting from a preceding scoping review and (2) identify barriers, facilitators and other considerations that may impact implementation. Literature on the development of complex health interventions and implementation science informed interview topics. Interviews were analyzed using generic coding and thematic analysis for open-ended and closed-ended questions, respectively. RESULTS: Stroke survivor and caregivers largely confirmed findings from the scoping review regarding intervention characteristics. Four main considerations for implementation were identified in interview analysis: (1) safety, (2) knowledge and beliefs, (3) environment, and (4) individual characteristics. CONCLUSION: It is essential to consider stroke survivor and caregiver perspectives when developing interventions to promote ongoing exercise following formal rehabilitation. The unique needs of each stroke survivor should be evaluated to optimize participation in WBTE.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".