Exercise perceptions among people with stroke: barriers and facilitators to participation
Bibliographic record
Abstract
BACKGROUND/AIMS: People with stroke continue to lead sedentary lives despite the multiple benefits of exercise. Knowledge regarding stroke survivors' conception of exercise, which is essential for a thorough understanding of the barriers and facilitators to exercise, is lacking. This study aims to explore the perceptions of exercise among stroke survivors, including their concepts and definitions of exercise, as well as their perceptions of barriers and facilitators to exercise. METHODS: This study utilized a qualitative exploratory design. Focus groups were conducted with 11 community dwelling participants with stroke (mean 8 ± 3 years post stroke). Data was analysed using content analysis methods. FINDINGS: Six themes that provided information regarding participants' perceptions of exercise were identified: mobility, balance, self-efficacy, outcomes, support, and exercise as part of an active or healthy lifestyle. Although participants internalized specific benefits of exercise, they did not demonstrate an internalized link between exercise and stroke risk. Low self-efficacy was identified as a barrier to participating in exercise, whereas, support to exercise (e.g., external encouragement, qualified personnel) was identified as a facilitator. CONCLUSION: These findings may provide insight when developing exercise interventions which optimize adherence for people with stroke.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".