Awareness and Use of Stroke Rehabilitation Interventions in Clinical Practice Among Physiotherapists
Bibliographic record
Abstract
Purpose: This study collected data about the current awareness and use of stroke rehabilitation interventions among a Canadian sample of physiotherapists as a foundational step toward future endeavours to inform the translation of rehabilitation research literature into practice. Methods: Participants were recruited from health care centres providing stroke rehabilitation to patients in each of the 10 provinces across Canada. Physiotherapists who provided direct rehabilitative care to individuals after a stroke, were ≥18 years old, and could read and write in English completed an electronic survey. Questions were asked about therapists’ work setting, patient demographics, how they stay up-to-date, and their awareness and use of stroke rehabilitation interventions. Results: One hundred seventy-five individuals (female = 82.9%) mainly from Ontario and Alberta (57.7%) were included. Therapists had high awareness and use of non-technological, peripherally-applied interventions (e.g., task-specific training, trunk training, overground walking). Except for mirror therapy and bilateral arm training, therapists had low or no awareness and use of brain priming interventions with or without a technological component. Conclusions: Therapists had low awareness and use of interventions that fall outside of standard education and training. This is an important area for future research on initiatives to increase knowledge translation and implementation into clinical practice.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".