Implementing Telerehabilitation After Stroke: Lessons Learned from Canadian Trials
Bibliographic record
Abstract
Introduction: Telerehabilitation has been promoted as a more efficient means of delivering rehabilitation services to stroke patients while also providing care options to those unable to attend conventional therapy. However, the application of telerehabilitation interventions in stroke populations has proven to be more challenging than anticipated, with many studies showing mixed results in terms of its efficacy. Six different clinical trials examining stroke telerehabilitation were initiated across Canada as part of the Heart and Stroke Foundation's 2013 Tele-Rehabilitation for Stroke Initiative, with interventions ranging from lifestyle coaching to delivering memory, speech, or physical training. The purpose of this article was to summarize the over-arching findings from this initiative, particularly the facilitators and barriers to the implementation of telerehabilitation services within a research context. Methods: Details of the projects were obtained directly from the study investigators and from materials published by each group. Qualitative open-ended questions were posed to each group for the discussion of lessons learned. Results: Important lessons learned from this initiative included: (1) the efficacy and cost of telerehabilitation is similar to that of traditional face-to-face management; (2) patients are satisfied with telerehabilitation services when trained appropriately and some social interaction occurs; (3) clinicians prefer face-to-face interactions but will use telerehabilitation when face-to-face is not feasible; and (4) technology should be selected based on ease of use and targeted to the skills and abilities of the users. Conclusions: Overall, results from these studies suggest that telerehabilitation services work best to augment face-to-face rehabilitation or when no other options are available.
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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 | 0.256 | 0.387 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.003 | 0.014 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".