Exploring physiotherapists’ knowledge and perception of exercise intensity in outpatient stroke rehabilitation: A qualitative study
Bibliographic record
Abstract
OBJECTIVES: To explore physiotherapists' knowledge and perception of exercise intensity and to identify factors influencing the attainment of recommended exercise intensity during rehabilitation sessions. METHODS: A qualitative study was conducted using semi-structured, face-to-face interviews with physiotherapists (PTs) who provide stroke rehabilitation within outpatient private settings. Data were audio-recorded, transcribed, and analysed using an inductive content analysis approach. RESULTS: Twelve physiotherapists (five women; median age: 44.5 years; median experience: 20 years) who routinely provide stroke rehabilitation participated in the study. Three overarching themes emerged: knowledge about exercise intensity, perception of provided exercise intensity and factors influencing the achievement of recommended intensity. Although 92% of PTs recognized the importance of exercise intensity, only 33% were aware of exercise intensity guidelines. A lack of knowledge regarding exercise intensity assessment, grading, and monitoring was commonly reported. Nearly half (41%) of the PTs reported providing moderate-to-vigorous intensity exercise to stroke survivors. Limiting factors to achieving recommended exercise intensity included patient-related factors (e.g., stroke severity, motivation, cognitive impairments, fatigue), work environment constraints (e.g., limited space), and PT-related challenges (e.g., lack of knowledge, limited contact with doctors). On the other hand, facilitators included high motivation among stroke survivors, large workspace, equipment availability, and the use of group-based training modalities. CONCLUSION: Achieving the recommended exercise intensity in outpatient stroke rehabilitation is influenced by personal, environmental, and PT-related barriers. Multifactorial facilitators, such as improved knowledge, enhanced workspaces, interdisciplinary collaboration, and group-based training approaches, should be considered by stakeholders to ensure the effective implementation of evidence-based stroke rehabilitation practices in private settings. Future research involving PTs with diverse educational backgrounds are needed to strengthen the transferability of these findings.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".