Barriers and Facilitators to Aerobic Exercise Testing Practices of Physiotherapists in In-Patient Stroke Rehabilitation Settings Across Canada: A Theory-Informed Web-Based Survey
Bibliographic record
Abstract
Purpose: Stroke best practice guidelines recommend aerobic exercise (AEx) testing once patients post-stroke are medically stable and before initiating AEx training. This study describes current AEx testing practice of physiotherapists in in-patient stroke rehabilitation (SR) and the barriers and facilitators to this practice. Method: We conducted a cross-sectional web-based survey of registered physiotherapists working in Canadian in-patient SR settings, guided by the Theoretical Domains Framework (TDF). Results: Fifteen of 37 (41%) participants reported conducting AEx testing with people post-stroke. A field test (92%) involving walking was the most commonly used test type. Barriers and facilitators to AEx testing practice from all 14 TDF domains were endorsed. While 73% (19/26) participants recognized that AEx testing supports therapy goals, over 60% reported lacking maximal and submaximal AEx testing knowledge and skills, and 58% did not perceive AEx testing to be an organizational priority due to the focus on function and mobility during in-patient SR. Conclusions: Less than half of participants performed AEx testing despite recognizing its value for people post-stroke. Predominantly, practitioner- and organisation-related factors influenced participants’ use of AEx testing in in-patient SR. An understanding of how physiotherapists can navigate the complex barriers to AEx testing is needed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".