Exploration of Implementation and Uses of Telerehabilitation in Physical Therapy in France During the COVID‐19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: To understand, through the study of telerehabilitation introduction, whether telerehabilitation in physical therapy ('masso-kinésithérapie') represents a lasting change in professional practices in France. METHODS: A triangulation of different qualitative methodologies was used, including eight semi-structured interviews, an observation of a telerehabilitation session and a participant and retrospective observation. A specific interview guide was developed for the interviews and another specific guide was developed for the telerehabilitation session observation to analyse this session. All semi-structured interviews were transcribed, and an in-depth thematic analysis based on grounded theory was performed. RESULTS: Three key themes emerged from our analysis. Firstly, physiotherapists quickly adopted telerehabilitation due to the widespread use of digital technologies and perceived clinical benefits. Secondly, they adapted their care delivery methods, transitioning from in-person to digital formats, and engaged in self-learning and peer support. These changes led to modifications in workspaces and communication practices, fostering closer relationships between physiotherapists and patients. Lastly, the implementation of telerehabilitation faces challenges, including technical difficulties, the lack of physical contact, and an unfavourable implementation context. CONCLUSION: Our results provide key elements to focus on for telerehabilitation implementation in physical therapy and professions for which professional practice is strongly associated with hands-on interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".