Facilitators and Barriers for the Adoption and Use of Telerehabilitation in Outpatient and Community Settings During the COVID-19 Pandemic: A Survey of Ontario Physiotherapists
Bibliographic record
Abstract
Purpose: To describe the impact of COVID-19 on the adoption and use of telerehabilitation (TR), and to identify facilitators and barriers of the provision in Ontario physiotherapy outpatient/community settings. Method: A cross-sectional design, web-based survey was disseminated to Ontario physiotherapists working in outpatient/community settings. Descriptive statistics were used for data analysis. Results: Responses from 243 physiotherapists were included in the analysis. Respondents reported increasing and initiating TR to maintain continuity of care and limit patient COVID-19 exposure. Facilitators for adopting TR were physiotherapists' attitudes and access to technology, convenience and ease of scheduling sessions, and perceived patient satisfaction and comfort in their home environment compared with in-person care. Patient-related barriers for adopting TR perceived by respondents included patients' attitude, suitability and ability to address their needs, ease of adoption, and Internet connectivity. More than 50% of respondents perceived that financial factors did not influence TR adoption. Conclusions: Physiotherapists increased their use of TR through the COVID-19 pandemic. Effective implementation of TR should include both patient and physiotherapist education, and best practice guidelines on implementation of TR in order to create a hybrid model of care that would better address the patient's needs.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".