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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".