Community Stroke Rehabilitation: How Do Rural Residents Fare Compared With Their Urban Counterparts?
Bibliographic record
Abstract
BACKGROUND: Rural living has been demonstrated to have an effect on a person's overall health status, and rural residing individuals often have decreased access to health and specialized rehabilitation services. AIM: The aim of this study was to determine if there are differences in recovery from stroke between urban and rural-dwelling stroke survivors accessing an in-home, community-based, interdisciplinary, stroke rehabilitation program. METHODS: Data from a cohort of 1222 stroke survivors receiving care from the Community Stroke Rehabilitation Teams between January 2009 and June 2013 was analyzed. This program delivers stroke rehabilitation care directly in a person's home and community. Functional and psychosocial outcomes were evaluated at baseline, discharge, and six -month follow-up. A series of multiple linear regression analyses was performed to determine if rural versus urban status was a significant predictor of discharge and 6-month health outcomes. RESULTS: The mean age of the rural cohort was 68.8 (±13.1) years (53.6% male), and the urban cohort was 68.4 (±13.0) years (44.8% male). A total of 278 (35.4%) individuals were classified as living in a rural area using the Rurality Index for Ontario. In multivariate linear regression analysis, no significant differences on the Functional Independence Measure, the Stroke Impact Scale, the Hospital Anxiety and Depression Scale, or the Reintegration to Normal Living Index were found between urban and rural cohorts. CONCLUSIONS: When provided with access to a home-based, specialized stroke rehabilitation program, rural dwelling stroke survivors make and maintain functional gains comparable to their urban-living counterparts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".