Using telerehabilitation to support people with multiple sclerosis: A qualitative analysis of interactions, processes, and issues across three interventions
Bibliographic record
Abstract
Introduction It is important to understand the interactions, processes, and issues that occur within telerehabilitation interventions to inform research and practice. The aim of this study was to identify the difficulties reported and intervention features that were captured as helpful in the notes written by occupational therapists during a telerehabilitation trial. Method Administrative documentation in the form of 60 subjective, objective, assessment, and plan notes were collected. The trial examined the effectiveness of three teleconference-delivered interventions: physical activity alone, fatigue management with physical activity, and contact-control social support for people with multiple sclerosis. Results Five themes emerged: desiring change, taking action, experiencing difficulty, infrastructure support, and relief and appreciation. Desiring change captured therapists’ observations of clients’ desire to manage symptoms and improve participation; it was most apparent at the beginning and supported clients’ taking action as the interventions progressed. Therapists identified their own difficulties with group facilitation and time management and clients’ difficulties with some intervention materials. Infrastructure support was reported to be helpful in minimizing some of these difficulties. In the end, the therapists expressed relief that the clients appeared to benefit from and appreciate the interventions. Conclusion This study highlights the need for and importance of providing resources and training to support teleconference-delivered interventions in clinical 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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".