Community reintegration of stroke survivors: the effect of a community navigation intervention
Bibliographic record
Abstract
AIM: The overall aim of the proposed study is to examine a newly implemented navigation intervention intended to support stroke survivors' community integration during the first year following hospital discharge in four regions of Ontario, Canada. BACKGROUND: Stroke is a leading cause of disability worldwide. Stroke survivors living in the community require regular, ongoing follow-up to assess recovery, prevent deterioration and maximize health outcomes. Internationally published evidence, often conducted in large urban centres, suggests that community reintegration services are an important component of the continuum of care for stroke survivors. This evidence, however, often does not address the particular challenges inherent in smaller urban and rural contexts. DESIGN: The design of this 2-year mixed-method study will use cohort and focused ethnography. METHODS: The three stages of this study include: (1) collection of quantitative data to profile the health status, support and extent of community reintegration of stroke survivors; (2) collection of qualitative data from stroke survivors and their care partners about community reintegration and navigation; and following triangulation of findings (3) knowledge translation activities. This study was ethically approved by the academic Research Ethics Board and clinical Research Ethics Board (Sudbury, Ontario) and funded by the Ontario Stroke Network (Canada). DISCUSSION: Results will describe experiences and outcomes of a community navigation intervention. Engagement of multiple stakeholders has the potential to develop a shared understanding of community reintegration and generate evidence informed recommendations for service enhancement at critical points in stroke recovery to support survivor and community well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".