Digital Health Technology for Stroke Rehabilitation in Canada: A Scoping Review
Bibliographic record
Abstract
(1) Background: Digital Health Technology (DHT) is an emerging method for stroke rehabilitation that could potentially be very effective in solving different problems in the therapeutic process. This study aims to explore the use of DHT for stroke rehabilitation in Canada, providing insights into how current technologies have been implemented and identifying gaps to inform future decision-making in clinical, research, and policy settings in the Canadian setting. (2) Methods: We followed the Arksey and O’Malley framework for scoping reviews. The original search was created in Medline (Ovid) and translated to PsycINFO (Ovid), Scopus, and CINAHL with Full Text (EBSCOhost). To locate grey literature, we searched Canadian Theses and Google. The search yielded 163 articles, of which we included 14 (8.6%) in the review. (3) Results: Fourteen studies published between 2010 and 2022 in Canada varied in design: 4 qualitative (28.6%), 4 randomized clinical trials (RCTs) (28.6%), 2 mixed methods (14.3%), and other types. The main goals included assessing intervention effectiveness (35.7%), client (28.6%) and clinician (28.6%) perceptions of technology, and feasibility (21.5%). Most studies focused on upper extremity (UE) function (85.71%), with some addressing walking speed (7.1%) and sitting balance (7.1%). Research mainly targeted the chronic phase of stroke (64.3%). Studies were conducted in home (50%) and institutional settings (42.9%). Technologies included sensors (50%), virtual reality (VR) (42.9%), games (28.6%), telerehabilitation (28.6%), and robots (14.3%). (4) Conclusions: This scoping review offers key insights into the use of DHTs for stroke rehabilitation in Canada, highlighting the types of technologies, their effectiveness, and the facilitators and barriers to adoption. These technologies show promise in improving patient outcomes, and their integration into Canadian healthcare systems presents a significant opportunity to enhance stroke rehabilitation.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.032 | 0.056 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".