Telerehabilitation Delivery in Canada and the Netherlands: Results of a Survey Study
Bibliographic record
Abstract
BACKGROUND: Following the onset of the COVID-19 pandemic, telerehabilitation (TR) has been expanding to address the challenges and risks of in-person delivery. It is likely that a level of TR delivery will continue after the pandemic because of its advantages, such as reducing geographical barriers to service. Many pandemic-related TR initiatives were put in place quickly. Therefore, we have little understanding of current TR delivery, barriers and facilitators, and how therapists anticipate integrating TR into current practice. Knowing this information will allow the incorporation of competencies specifically related to the use and provision of TR into professional profiles and entry-to-practice education, thereby promoting high-quality TR care. OBJECTIVE: This study aimed to obtain a descriptive overview of current TR practice among rehabilitation therapists in Canada and the Netherlands and identify perceived barriers to and facilitators of practice. METHODS: A web-based cross-sectional survey was conducted with occupational, physical, and respiratory therapists and dietitians in Canada (in French and English) and the Netherlands (in Dutch and English) between November 2021 and March 2022. Recruitment was conducted through advertisements on social media platforms and email invitations facilitated by regulatory and professional bodies. The survey included demographic and practice setting information; whether respondents delivered TR, and if so, components of delivery; confidence and satisfaction ratings with delivery; and barriers to and facilitators of use. TR satisfaction and uptake were measured using the Telehealth Usability Questionnaire and modified Technology Acceptance Model. Data were first summarized descriptively, and then, comparisons were conducted between professions. RESULTS: Overall, 723 survey responses were received, mostly from Canada (n=666, 92.1%) and occupational therapists (n=434, 60%). Only 28.1% (203/723) reported receiving specific training in TR, with 1.2% (9/723) indicating that it was part of their professional education. Approximately 19.5% (139/712) reported not using TR at all, whereas most participants (366/712, 51.4%) had been using this approach for 1 to 2 years. Services delivered were primarily teleconsultation and teletreatment with individuals. Respondents offering TR were moderately satisfied with their service delivery and found it to be effective; 90.1% (498/553) indicated that they were likely to continue offering TR after the pandemic. Technology access, confidence, and setup were rated the highest as facilitators, whereas technology issues and the clinical need for physical contact were the most common barriers. CONCLUSIONS: Professional practice and experience with TR were similar in both countries, suggesting the potential for common strategic approaches. The high prevalence of current practice and strong indicators of TR uptake suggest that therapists are likely to continue TR delivery after the pandemic; however, most therapists (461/712, 64.7%) felt ill prepared for practice, and the need to target TR competencies during professional and postprofessional education is critical. Future studies should explore best practice for preparatory and continuing education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".