Canadian Physiotherapists Integrate Virtual Care during the COVID-19 Pandemic
Bibliographic record
Abstract
Purpose: To examine Canadian physiotherapists' experiences in adapting their delivery of patient care during the COVID-19 pandemic. We examine the level of strain on the profession and barriers and enablers to virtual care and provide strategies to support future virtual care implementation. Methods: From May to October 2020, a series of eight cross-sectional survey cycles were distributed every 2-4 weeks through branches and divisions of the Canadian Physiotherapy Association, social media, and personal networks. Descriptive statistics summarized the main findings. Open ended questions were first analyzed inductively using thematic analysis, then deductively mapped to the Capability-Opportunity-Motivation Behavioural (COM-B) Model. Results: Between 1,820 (cycle 1) and 334 (cycle 7) physiotherapists responded. Median strain level was 5/5 (cycle 1) and dropped to median 3/5 (cycles 5-8). In cycle 1, 55% of physiotherapists had ceased in-person care, while 41% were providing modified in-person care. Of these physiotherapists, 79% were offering virtual care. As modified in-person care increased, virtual care continued as a substantial aspect of practice. Physiotherapists identified barriers (e.g., lack of hands-on care) and enabling factors (e.g., greater accessibility to patients) for virtual care. In-depth examination of the barriers and enablers through the COM-B lens identified potential interventions to support future virtual care implementation, including education and training resources for physiotherapists and communication and advocacy to patients and the public on the value of virtual care. Conclusions: Canadian physiotherapists exhibited high adaptability in response to COVID-19 through the rapid and widespread use of virtual care. By creating an in-depth understanding of the barriers and enablers to virtual care, along with potential interventions, this work will facilitate future opportunities to support and enhance physiotherapists' delivery of virtual care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".