Stroke Rehabilitation and Patients with Multimorbidity: A Scoping Review Protocol
Bibliographic record
Abstract
Stroke care presents unique challenges for clinicians, as most strokes occur in the context of other medical diagnoses. An assessment of capacity for implementing "best practice" stroke care found clinicians reporting a strong need for training specific to patient/system complexity and multimorbidity. With mounting patient complexity, there is pressure to implement new models of healthcare delivery for both quality and financial sustainability. Policy makers and administrators are turning to clinical practice guidelines to support decision-making and resource allocation. Stroke rehabilitation programs across Canada are being transformed to better align with the Canadian Stroke Strategy's Stroke Best Practice Recommendations. The recommendations provide a framework to facilitate the adoption of evidence-based best practices in stroke across the continuum of care. However, given the increasing and emerging complexity of patients with stroke in terms of multimorbidity, the evidence supporting clinical practice guidelines may not align with the current patient population. To evaluate this, electronic databases and gray literature will be searched, including published or unpublished studies of quantitative, qualitative or mixed-methods research designs. Team members will screen the literature and abstract the data. Results will present a numerical account of the amount, type, and distribution of the studies included and a thematic analysis and concept map of the results. This review represents the first attempt to map the available literature on stroke rehabilitation and multimorbidity, and identify gaps in the existing research. The results will be relevant for knowledge users concerned with stroke rehabilitation by expanding the understanding of the current evidence.
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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 | 0.073 | 0.064 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.065 | 0.010 |
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".