Practice Makes Perfect : Acceptability and Feasibility of a Telerehabilitation Solution For Rehabilitation Stroke Teams
Bibliographic record
Abstract
Telerehabilitation solutions i.e. interactive virtual encounters, using video plus audio to emulate face-to-face rehabilitative treatments, have been more and more studied to improve patientsu2019 clinical outcomes and costs associated with stroke rehabilitation. However, data on the perception of the professionals who use the solution is lacking, particularly during the rehabilitation phase of stroke care continuum. This study aimed to explore the acceptability and feasibility of a telerehabilitation solution, from a professionalu2019s perspective.Two rehabilitation teams (including healthcare professionals and coordinators, n total = 17) from two different regions in Quebec, Canada, were interviewed after a five month pilot trial of a telerehabilitation solution. The interview guide was developed using a theoretical framework on the acceptability of healthcare interventions. Interviews were verbatim transcribed and analyzed for content.Five emerging themes were identified around the concept of acceptability: 1) learning curve and need to develop training- and intervention-related material; 2) intensity of interaction with patients 3) appropriate mix of face-to-face and telerehabilitation sessions; 4) using patientsu2019 home features and exercise equipment; and 5) team scheduling made easy. Lessons learnt in terms of feasibility will also be presented.The telerehabilitation solution was positively received despite a certain degree of apprehension from the participants in the early phase of the trial. This study sheds light on the context and tools that need to be put in place to optimize the implementation of a telerehabilitation solution as part of a stroke care continuum.
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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 | 0.040 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".