Virtual Physical Therapy Practices in Canadian Exercise-Based Pulmonary Rehabilitation Programmes
Bibliographic record
Abstract
Purpose: To understand the current physical therapy virtual care practices in Canadian pulmonary rehabilitation programmes. Method: Exercise-based pulmonary rehabilitation programmes were identified through Canadian pulmonary associations available on public websites. Physiotherapists at eligible sites were emailed a web-based survey (REDCap) between March and June 2023. Results: = 20, 65%) reported using a hybrid (mix of virtual and in-person) delivery model in the preceding 6 months. Assessments of exercise capacity, respiratory status, exertional oxygen requirements, and physical function were primarily conducted in-person, whereas exercise prescription and progression, respiratory treatment, psychosocial support, vital sign monitoring, and education were conducted both in-person and virtually. Commonly reported enablers to virtual care were patient preference, medical stability, and availability of technological equipment. Clinical practice guidelines alongside continuing education and training opportunities were identified as important resources needed to optimize virtual or hybrid rehabilitation delivery. Conclusions: Following the COVID-19 pandemic there has been a shift towards a hybrid model of exercise-based pulmonary rehabilitation delivered by physiotherapists. Expanding the evidence base particularly around the clinical utility of virtual assessment may further inform virtual care practices.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".