Use of videoconferencing for physical therapy in people with musculoskeletal conditions: A systematic review
Bibliographic record
Abstract
Background Physical therapists are key players in the management of musculoskeletal conditions, which are common in rural and remote communities. There are few physical therapists in rural regions compared to potential need, so care is either not provided or must be sought in urban centers, requiring travel and time away from work and family to access services. Telerehabilitation strategies, such as real-time videoconferencing, are emerging as possible solutions to address shortages in rural physical therapy services. Objectives This review will: (1) determine the validity and the reliability of secure videoconferencing for physical therapy management of musculoskeletal conditions; (2) determine the health, system, and process outcomes when using secure videoconferencing for physical therapy management of musculoskeletal conditions. Methods A protocol-driven systematic review of four databases was carried out by two independent reviewers. Study criteria included English language articles from January 2003 to December 2016, on physical therapy management using secure videoconferencing, pertaining to adults 18-80 years with chronic musculoskeletal disorders. Randomized controlled trials, pre-experimental studies, and case-control studies were included. Quality analysis was performed utilizing standardized tools specific for the study designs. Results and conclusions Validity and reliability studies were identified as having high risk of bias. Intervention studies were of moderate quality, and found positive impact on health outcomes and satisfaction. Two studies evaluated costs, with evidence of cost savings in one study. More robust research is required to evaluate long-term effects of telerehabilitation for physical therapy management of musculoskeletal disorders, including cost-benefit analyses.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".