COMMUNITY STROKE NAVIGATION DIFFERENT APPROACHES ACROSS ONTARIO, CANADA
Bibliographic record
Abstract
Stroke Community Navigation has recently become an integral part of the stroke care system in Ontario putting into action a number of Stroke Best Practice Guidelines, notably supporting stroke clients through transitions and facilitation of community reintegration. Navigators are trained, culturally sensitive, health professionals providing holistic case management to help improve the quality of life. Community Stroke Navigators help to ease the adjustment to post-stroke life for survivors and their families. Navigators increase capacity and performance of the health care system by improving access to much needed services and resources for patients and families. Navigators work to eliminate barriers, provide education, and facilitate connections to services such as transportation, in-home nursing, personal care, adaptive equipment, home modifications, community engagement, as well as physical, occupational and/or mental health therapies at different times along the stroke care continuum as needed. This collaborative presentation aims to provide an overview of the development of three Stroke Community Navigation programs across the province of Ontario with a focus on the successes and challenges navigators face working as part of different models of care such as hospital versus community based approaches and urban versus rural service provision. There will also be discussion on how these programs collaborate with one another as part of a larger national group.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".