Looking Across Ontario : How Stroke Community Navigators Are Using Canadian Best Practice Guidelines To Improve Patient Outcomes
Bibliographic record
Abstract
A Stroke Community Navigator (SCN) is a healthcare professional that provides support for stroke survivors and their family to positively enhance the transitions across the continuum of care. The services provided by SCNs vary across Ontario, depending on the specific needs of the region. This e-poster will compare Stroke Navigation across 3 diverse regions in Ontario; including West Greater Toronto Area, Windsor- Essex County and North- East Ontario. The e-poster will provide an overview of the role of SCN, services provided, work setting, number of clients served and the assessment tools utilized, as well as the alignment of each variable with Canadian Best Practice Guidelines (CBPG). Trained and committed SCNs provide holistic care and guidance which helps to improve the stroke recovery experience and improve the clientu2019s quality of life. They are able to ease the adjustment to post-stoke life through education, improving access to healthcare services and connections to appropriate care providers. Both rural and urban population are supported through SCNs who work in various clinical area including but not limited to; acute care, rehabilitation units, outpatient clinics and the community at large. These three centers provide stroke navigation through healthcare professionals who integrate the CBPGs for stroke into their models for delivering care. The e-poster will show how three regions have implemented the CBPGs to meet the various needs of their unique communities. It will further highlight the essential elements of navigation which support clients at various stages of transitions along the care continuum.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".