Enhancing Community-Based Rehabilitation for Stroke Survivors: Creating a Discharge Link
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Intensive poststroke rehabilitation is critical to maximizing outcomes, improving inpatient flow, and decreasing long-term costs. This regional initiative investigated the impact of improving access to timely intensive home-based stroke rehabilitation follow-up care on hospital length of stay (LOS) and readmission rates. The enhanced service was made available across the region's rural geography to new stroke survivors transitioning home who were unable to access outpatient care. METHODS: All new stroke survivors with ongoing rehabilitation needs being discharged from the hospital and eligible for home care received timely enhanced intensity of home-based rehabilitation services from existing community rehabilitation providers for 2 months after discharge. Five hundred twenty-four stroke survivors received this service over a 3-year period. The service priority rating was increased to reduce wait times to less than 5 days. Collaborative planning across sectors occurred through discharge link meetings. Comparative analysis was used to investigate health system measures before and after service implementation. RESULTS: A 15.7-day decrease in hospital LOS and decreased hospital readmission rates were observed after enhanced service implementation. Functional Independence Measure (FIM) efficiency improved for those discharged from inpatient rehabilitation. Average wait time for community rehabilitation services decreased from 44 days to 4.4 days. The mean total number of community therapy visits more than doubled to an average of 12 per client. CONCLUSION: The provision of timely more intensive home-based rehabilitation services for new stroke survivors transitioning home and unable to access outpatient care was associated with decreased hospital LOS and decreased readmission rates.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".