Enhancing physiotherapists' knowledge and perceptions of telerehabilitation: A before‐after educational intervention study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In the evolving landscape of healthcare, telerehabilitation is emerging as a pivotal modality, especially in delivering services to vulnerable populations. With the increasing reliance on digital health solutions, there is a pressing need for physiotherapists to be adequately trained in telerehabilitation. This training is essential for them to adapt to new technologies and methodologies, ensuring effective and efficient patient care. The aim of this study was to evaluate the effect of a telerehabilitation educational intervention on physiotherapists' knowledge and perceptions in Bucaramanga and its metropolitan area. METHODS: A group of 27 physiotherapists underwent an educational intervention focused on telerehabilitation. Before- and after-intervention assessments were conducted to gauge their perceptions and knowledge. RESULTS: Participants generally held a positive perception of telerehabilitation both before and after the intervention [Before Median (Md) and interquartile range (IQR): Md = 2.5 (IQR = 2.1-3); after: Md = 2.7 (IQR = 2.4-3.1), p = 0.256]. A significant increase in their knowledge after-intervention was observed [Before: Md = 55.5 (IQR = 33.3-66.6)]; after: Md = 77.7 (IQR = 66.6-88.8), p = <0.001, emphasizing the potential benefits of targeted educational interventions. CONCLUSIONS: The educational intervention significantly improved physiotherapists' knowledge of telerehabilitation, underscoring the importance of professional training in this domain. While perceptions remained consistently positive, the notable increase in knowledge suggests that such educational programs are crucial for enhancing the adoption and effective use of telerehabilitation in physiotherapy practice.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".