Telerehabilitation Implementation: Perspectives from Physiotherapists Working in Complex Care
Bibliographic record
Abstract
Purpose: The COVID-19 pandemic resulted in a rapid change in ways clinicians deliver physiotherapy services, leading to an important uprise in telerehabilitation implementation. Sharing the experiences of physiotherapists in clinically adopting this technology during this initial wave of the pandemic can influence future implementation. This mixed-method study aimed to identify the barriers and new facilitators of telerehabilitation clinical implementation. Method: Canadian physiotherapists with and without telerehabilitation experience, working in various clinical settings, were recruited during the first wave of the COVID-19 pandemic. Participants completed the Assessing Determinants of Prospective Uptake of Virtual Reality instrument (ADOPT-VR) adapted for telerehabilitation and participated in online focus groups to explore their experiences with telerehabilitation implementation. Demographic data and ADOPT-VR responses were analyzed descriptively. Qualitative data were analyzed using content analysis. Results: Sixteen physiotherapists completed the study. Scores on the Likert scale showed that physiotherapists enjoyed telerehabilitation (7.5/10) and perceived it as being useful (7.3/10). Physiotherapists disagreed with the necessity to use only minimal mental efforts (4.4/10) and feeling familiar with the evidence (4.7/10). Limited access to telerehabilitation implementation evidence, a reduced hands-on approach, and a lack of validated remote assessments were reported as barriers. Clinical practice guidelines, validated remote neurological assessments, changes in physiotherapy curriculum, and policy-making are critical to improving telerehabilitation implementation within physiotherapy practices. Conclusions: Participants positively experienced the quick use of telerehabilitation from the beginning of the COVID-19 pandemic, but some important barriers remain.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".