Stroke Rehabilitation in Canada: A Work in Progress
Bibliographic record
Abstract
Stroke rehabilitation in Canada continues to function under models and practices that have changed little in the last four decades and struggles to implement new evidence-based or best practices. Ontario, Canada's largest province, has had a coordinated stroke strategy since 2000. The Ontario Stroke System has developed an extensive infrastructure of research syntheses, consensus panel recommendations, practice guidelines, standards of care, and centralized data collection across the continuum of stroke care. This has produced a solid foundation upon which an evidence-based stroke rehabilitation system can be developed. However, failure to invest in stroke rehabilitation or provide incentives to implement change has resulted in the stroke rehabilitation system and critical outcomes remaining largely unchanged. Improvements in time to admission have been countered by rising admission FIM scores such that severe stroke patients often cannot access the stroke rehabilitation system. Many stroke patients are still rehabilitated on general rehabilitation units, therapy intensities remain unacceptably low, and many outpatient programs are being reduced or even closed. Although there are pockets of innovation, the stroke rehabilitation system continues to function more according to traditional ways of practicing. The hope is that with appropriate investments and incentives, Canadians and Ontarians can build upon the existing infrastructure to ensure stroke patients receive optimal rehabilitative care based on best evidence. In the meantime, stroke rehabilitation in Canada remains a work in progress.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".