Mainstream Telerehabilitation: A Threat to the Physiotherapy Profession or a Paradigm-Shifting Opportunity?
Bibliographic record
Abstract
Purpose: This study explored physiotherapists' attitudes toward telerehabilitation (TR) focusing on how TR adoption during COVID-19 impacted the physiotherapy (PT) profession and the sustained use of TR treatment models. Method: A survey mainly comprised of open-ended questions was administered to Canadian physiotherapists in private practice. The responses were analyzed with inductive content analysis to generate themes reflecting barriers and opportunities associated with TR. Results: Among barriers, the sub-themes of challenges with technology, technical know-how, patient buy-in, and professional identity emerged. Among opportunities, the sub-themes of patient empowerment and self-management, positive treatment outcomes, increased access, expanded skillset, and paradigm shifting emerged. A unique finding was the tension between the themes of TR threatening and narrowing physiotherapists' professional identities versus expanding PT practice with hands-off care models enabled by TR. Conclusions: This study corroborated past research showing technological and know-how barriers to TR adoption and suggested that TR has moved PT practice toward less reliance on passive therapies. An important implication of the study is that while TR may facilitate a paradigm shift toward patient self-management, buy-in may impede the sustainability of TR and other hands-off treatment models.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".