Use of real-time videoconferencing to deliver physical therapy services: A scoping review of published and emerging evidence
Bibliographic record
Abstract
INTRODUCTION: Telehealth may be a viable means to deliver physical therapy services across a range of practice settings and health conditions; however, there is limited uptake of telehealth in clinical practice. The purpose of this study is to examine and describe trends, gaps and opportunities in published and emerging evidence regarding the use of real-time videoconferencing to deliver physical therapy services. METHODS: Four databases and three trial registries were searched using terms for physical therapy and telehealth. Inclusion criteria were primary studies, systematic reviews and published trial registries that had the following features: physical therapy assessment and/or treatment, real-time videoconferencing and English language. Title/abstract, full text screening and data extraction were completed by pairs of independent reviewers. Descriptive statistics stratified by published research and trial registry records were used to summarize study characteristics. RESULTS: A total of 100 studies (80 published and 20 trial registries) were included. Australia, Canada and the US have the highest proportion of published and emerging research (63%). The majority of conditions studied were musculoskeletal (42%). Computers were the most common videoconferencing technology used (31%) and only 14% of studies reported using a secure platform. The majority of studies examined health outcomes (64%) and process outcomes (65%), while only 32% reported system outcomes. DISCUSSION: Research in the field of telehealth and physical therapy is growing and becoming increasingly diverse with the advancements in technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.032 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".