The Era of Technology in Healthcare—An Evaluation of Telerehabilitation on Client Outcomes: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
PURPOSE: This systematic review and meta-analysis aimed to synthesize the evidence and examine the effect of telerehabilitation interventions compared to face-to-face rehabilitation interventions on physical functioning, mental health, and pain reduction among employed individuals, 18 years old and older. METHODS: Following the Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search syntax was created and inputted into Ovid Medline, APA PsycINFO, Ovid Embase, CINAHL, and Scopus. Critical appraisal of the included studies was conducted by two researchers to assess the risk of bias. A meta-analysis was completed for the randomized controlled trials and GRADE was used to determine the certainty of the evidence. RESULTS: A total of 16 out of 4319 articles were included in this review. This systematic review and meta-analysis found no significant differences between telerehabilitation interventions for physical functioning, mental health, and pain reduction outcomes compared to traditional rehabilitation interventions. CONCLUSION: The study findings indicate that telerehabilitation is less effective than in-person care for occupational therapy and physical therapy services. Future research may look at addressing the limitations of the current study to produce more conclusive results, such as exploring the length of the intervention, knowledge and confidence of intervention application, and follow-ups. SYSTEMATIC REVIEW REGISTRATION: This systematic review has been registered with PROSPERO under registration number CRD42022297849 on April 8th, 2022.
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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 | 0.042 | 0.093 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.047 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 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".