Satisfaction With In-Home Speech Telerehabilitation in Post-Stroke Aphasia: an Exploratory Analysis
Bibliographic record
Abstract
Patient satisfaction with healthcare has a major impact on clinical outcomes and compliance. Satisfaction with telehealth services for speech and language problems has been documented but not in post-stroke aphasia. The main objective here was to evaluate patient satisfaction with speech tele rehabilitation based on the PACE pragmatic rehabilitation approach in post-stroke aphasia. This study was embedded in a pre-/post-test feasibility and efficacy study in which 20 patients with chronic post-stroke aphasia received 3 weeks of speech therapy (9 sessions) through in-home tele-rehabilitation. A telerehabilitation platform based on a commercial videoconferencing system (Tandberg 550 MXP) with custom software was used to transmit audio, video and data through a high-speed Internet connection between the participant’s home and the clinician. Participants’ satisfaction with in-home telerehabilitation and healthcare received was assessed using French adaptations of the Telemedicine Satisfaction Questionnaire and Health Care Satisfaction Questionnaire. Satisfaction with functional communication, i.e. communication in common situations of daily life, was compared pre- and post-intervention with participants and caregivers. Participants’ satisfaction with in-home telerehabilitation was excellent (94%±4.3%). Satisfaction with healthcare received was good overall (80%±11.4%) and for three factors measured independently, i.e. relationship with healthcare professional (84%±12.5%), services delivered (73%±13.8%), and general healthcare organization (84%±12.0%). Participants’ and caregivers’ satisfaction with communication was higher after the intervention (p=0.001 and p<0.001, respectively) and was correlated with age (r=-0.60; p=0.007). Patients with post-stroke aphasia receiving speech tele-therapy were very satisfied with this service delivery method. Also, technology use was not an issue for seniors post-stroke.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".