Facilitating Community-Based Exercise for People With Stroke: Cross-Sectional e-Survey of Physical Therapist Practice and Perceived Needs
Bibliographic record
Abstract
BACKGROUND: Educating people with stroke about community-based exercise programs (CBEPs) is a recommended practice that physical therapists are well positioned to implement. OBJECTIVE: The aim of this study was to evaluate the provision of education about CBEPs to people with stroke, barriers to providing education, and preferences for resources to facilitate education among physical therapists in neurological practice. DESIGN: A cross-sectional e-survey of physical therapists treating adults with stroke in Ontario, Canada, was conducted. METHODS: A link to the questionnaire was emailed to physical therapists in a provincial stroke network, a provincial physical therapy association, and on hospital and previous research lists. RESULTS: Responses from 186 physical therapists were analyzed. The percentage of respondents who reported providing CBEP education was 84.4%. Only 36.6% reported typically providing education to ≥7 out of 10 patients with stroke. Physical (90.5%) and preventative (84.6%) health benefits of exercise were most frequently discussed. Therapists reported most commonly delivering education at discharge (73.7%). Most frequently cited barriers to educating patients were a perceived lack of suitable programs (53.2%) and a lack of awareness of local CBEPs (23.8%). Lists of CBEPs (94.1%) or brochures (94.1%) were considered to be facilitators. The percentage of physical therapists providing CBEP education varied across acute, rehabilitation, and public outpatient settings. LIMITATIONS: The percentage of physical therapists providing education may have been overestimated if respondents who deliver CBEP education were more likely to participate and if participants answered in a socially desirable way. CONCLUSIONS: Even though a high proportion of physical therapists provide CBEP education, education is not consistently delivered to the majority of patients poststroke. Although a CBEP list or brochure would facilitate education regarding existing CBEPs, efforts to implement CBEPs are needed to help overcome the lack of suitable programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".