Physiotherapists’ Adoption and Perceptions of Tele-Rehabilitation for Cardiorespiratory Care in Response to COVID-19
Bibliographic record
Abstract
Purpose: The use of tele-rehabilitation as a mode for physiotherapy services was widely implemented following the onset of the coronavirus disease 2019 (COVID-19) pandemic. This study explored the perceived value and experiences of physiotherapists relating to tele-rehabilitation for cardiorespiratory care. Method: Semi-structured interviews were conducted with physiotherapists who provided tele-rehabilitation to adults with cardiorespiratory conditions between March 11 and December 31, 2020. Interviews were analyzed using conventional content analysis. Results: Seven participants were interviewed; six practising solely in pulmonary rehabilitation and one practising in both pulmonary and cardiac rehabilitation. Three major themes emerged: (1) the pandemic presented unique challenges to implementing tele-rehabilitation while exacerbating previous challenges inherent with virtual care, (2) tele-rehabilitation use during the pandemic was deemed as equally effective in quality of care and patient adherence when compared to in-person services, and (3) tele-rehabilitation had significant value during the pandemic and has potential as an alternative delivery model post pandemic. Conclusion: Despite the inherent challenges, tele-rehabilitation was endorsed by participants as a suitable and effective alternative to care delivery and holds promise as a post-pandemic delivery model. Further evaluation is needed to support and optimize tele-rehabilitation use in physiotherapy 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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".