Télésoin en orthophonie depuis sa pérennisation en France : état des lieux et retours d’expérience des orthophonistes, patients et aidants
Bibliographic record
Abstract
In a state of experimentation for a few years, telepractice in speech therapy has expanded throughout France during the health crisis in 2020. Thus, many professionals have been able to discover remote care. This modern technique, widely used in some countries such as Canada or the United States, is officially perpetuated in France thanks to the endorsement 17 published on April 13, 2021 in the Journal Officiel. This amendment allows speech therapists to continue to practice remotely on a conventional basis under certain conditions. Therefore, it seemed relevant to establish an overview of this practice in France, which is now an integral part of speech therapy, and to evaluate the satisfaction of those who have already tried it. To do this, online questionnaires were distributed to speech therapists, patients and their caregivers. 237 responses from speech therapists as well as 40 responses from patients and caregivers were analyzed. The main results of the study show that the profiles of speech therapists using telepractice have not really changed since the beginning of the health crisis. In addition, speech therapists who continue to use telepractice today appear to be satisfied overall. Conversely, speech therapists who stopped using it did not like it. Patients and caregivers have a good level of satisfaction, whether or not they have continued the 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".