What are the perceptions and lived experiences of Canadian injured workers about the provision of physiotherapy services using telerehabilitation?
Bibliographic record
Abstract
Purpose Despite evidence of efficacy, the effectiveness of telerehabilitation in real-world clinical settings is still largely unknown. Telerehabilitation requires a substantial transformation of the organization and delivery of traditional services. Considering that a virtual setting can create unique challenges for providing physiotherapy services and given the physical and potential hands-on nature of evidence-based assessments and interventions, it is important to investigate what injured workers think of receiving physiotherapy care via telerehabilitation and to examine if rehabilitation needs are adequately met. Methods A qualitative interpretive description study was conducted to explore the perspectives and experiences of 17 injured workers receiving physiotherapy via telerehabilitation. Data were collected through semi-structured interviews with participants from three provinces in Western Canada and analysed using thematic analysis. Qualitative rigour criteria of epistemological integrity, analytic logic, interpretive authority, and representative credibility were considered throughout this study. Results Implementation of telerehabilitation during the COVID-19 pandemic resulted in mixed perceptions from injured workers. Some viewed telerehabilitation as a resourceful option for providing services during the pandemic lockdown, resulting in maintained communications while overcoming barriers to services (e.g., rural/remote workers, transportation barriers, etc.). However, many thought telerehabilitation was inferior to in-person therapy for assessment and when ‘handson’ interaction was needed. Many believed a hybrid option may be ideal now that pandemic restrictions are lifted, with telerehabilitation supplementing in-person physiotherapy when needed. Conclusions Telerehabilitation was viewed as a resourceful option during the pandemic and in certain clinical situations (e.g., rural/remote). Workers should be able to make informed choices about service delivery format.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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 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".