Physiotherapists’ use of aerobic exercise during stroke rehabilitation: a qualitative study using chart-stimulated recall
Bibliographic record
Abstract
PURPOSE: We aimed to explore the factors that affected physiotherapists' use of aerobic exercise during stroke rehabilitation for people with stroke. MATERIAL AND METHODS: We conducted a qualitative descriptive study using thematic analysis informed by a pragmatic worldview. Physiotherapists attended one on one semi-structured interviews to answer some general questions about aerobic exercise and then discussed the charts of their four most recently discharged clients with stroke. Both deductive and inductive coding were used for analysis. RESULTS: Ten physiotherapists participated. Healthcare policies and limited resources were mostly discussed in general questions while specific profiles of clients with stroke, their goals and preferences were mostly discussed in patient specific questions. Three themes were identified: (1) physiotherapists' perspectives and practices regarding aerobic exercise; (2) profiles of people with stroke, as well as their goals and their exercise modality preferences; and (3) influence of health system priorities, rehabilitation intensity policy, and resources. CONCLUSIONS: Physiotherapists' behaviours regarding use of aerobic exercise for people with stroke are not a binary behaviour of prescribing or not prescribing aerobic exercise. Their behaviours are better understood on a continuum; between two ends of not prescribing aerobic exercise, and prescribing aerobic exercise with defined intensity, duration, and frequency.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".