Perceived Barriers and Facilitators of Using Synchronous Telerehabilitation of Physical and Occupational Therapy in Musculoskeletal Disorders: A Scoping Review
Bibliographic record
Abstract
ABSTRACT Purpose Physical and occupational therapy interventions are increasingly delivered through videoconferencing to overcome barriers related to face-to-face delivery. The objective of this scoping review was to identify barriers and facilitators of using synchronous telerehabilitation to deliver these interventions for musculoskeletal disorders. Materials and Methods MEDLINE, EMBASE, PsycInfo, CINAHL, Cochrane Library, and ProQuest Dissertations and Theses databases were searched in May 2020. Qualitative and quantitative studies in any language that described barriers and facilitators of using synchronous videoconferencing for physical or occupational interventions or assessments for individuals with musculoskeletal diseases were eligible. Results Twenty-three publications were included that reported 59 facilitators and 41 barriers to using telerehabilitation. All included studies (100%) reported on facilitators, and 20 (87%) studies also reported on barriers. Most commonly reported facilitators included convenience and accessibility of services, audio and visual quality, and financial and time savings. Most commonly reported barriers included technological issues, privacy concerns, impersonal connection, and difficulty establishing rapport between patients and healthcare professionals. Conclusions Factors including quality and user-friendliness may facilitate the delivery of physical or occupational therapy interventions or assessments for musculoskeletal diseases using telerehabilitation. Strategies to address key barriers should be considered when developing and implementing such interventions or assessments. Implications for rehabilitation Videoconferencing with a healthcare professional can be an effective way to deliver patient-centered physical or occupational therapy telerehabilitation interventions. Strategies to combat barriers to using telerehabilitation may include using a stable, high-quality videoconferencing platform, enhancing self-efficacy to using videoconferencing amongst patients and health care providers, and addressing concerns related to privacy. During the current COVID-19 pandemic, the present study provides insight into the successful development and delivery of physical or occupational telerehabilitation interventions for at-risk populations.
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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 | 0.021 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".