A survey of the experiences of delivering physiotherapy services through telerehabilitation during the COVID-19 pandemic
Bibliographic record
Abstract
Introduction: Physiotherapy services have been typically provided in-person since the profession usually involves a therapist providing hands-on assessment and treatments. The COVID-19 pandemic provided an opportunity to study physiotherapists' adaptation to telerehabilitation (phone or videoconference). Objective: This study aimed: (1) to explore how physiotherapists adapted to the transition to delivering telerehabilitation, (2) to assess physiotherapists' perceptions of implementing telerehabilitation, and (3) to identify the challenges and facilitators of delivering telerehabilitation. Methods: This study used an online survey distributed to physiotherapists within a large Canadian health authority. Closed-ended questions were analyzed with descriptive statistics. Results: Seventy-five physiotherapists responded and data were collected. Compared prior to the pandemic to time during the pandemic, the use of a phone for delivering physiotherapy increased from 24.0% to 73.3% of physiotherapists while videoconference increased from 5.3% to 77.3%. Overall, the physiotherapists found videoconference to be a more effective delivery method than phone. Less than half felt that they could use videoconference to effectively treat pain (49.3%), upper extremity function (40.0%) or strength/range of motion (48.0%). Only 29.3% felt that they could effectively treat walking balance or mobility by videoconference. Technical barriers were identified with client comfort with the equipment reported by 90.7% of physiotherapists and positioning of the webcam by 76.0% of physiotherapists. A large proportion of physiotherapists agreed that they would continue the practice of telerehabilitation via phone (54.7%) and videoconference (68.0%). Conclusion: The pandemic resulted in a dramatic shift to telerehabilitation for a profession that typically provides hands-on assessments and treatments. While there was increased uptake of telerehabilitation, many physiotherapists questioned their effectiveness using telerehabilitation to undertake activities that traditionally involve manual treatments or hands-on guidance/supervision. However, physiotherapists were committed to continuing telerehabilitation to meet patients' needs after the pandemic.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".