CROSS SECTOR COLLABORATION TO ACHIEVE INNOVATION IN MULTIDISCIPLINARY CLINICAL REHABILITATION
Bibliographic record
Abstract
In Ontario, Canada the hospital and the community sectors are funded separately and opportunities for collaboration are not always explored to their full potential. In 2017, Central West Local Health Integration Network (CWLHIN) Home and Community Care partnered with William Osler Health System (WOHS) to co-design an integrated approach to meet best practice targets for mild stroke patients (Alpha FIM u00ae > 80). Shared funding between the hospital and community sectors was obtained to explore innovative ways of providing community stroke rehabilitation services. A review of stroke best practices and environmental scan of existing community rehabilitation programs was the starting point for this work. Creating a culture of innovation and patient centeredness within the project team and identifying and leveraging the strengths of the involved stakeholders were also critical to this work. A robust evaluation framework was developed to monitor the program. An interdisciplinary cross-sector clinical rehabilitation team was formed. This team had access to hospital records and the opportunity to treat the client in either the home or congregate settings based on patient goals and needs. Service levels provided by the rehabilitation team were aligned with the Canadian Stroke Best Practices. The team received joint orientation and training opportunities to improve stroke knowledge. Problems that arose during the program implementation were addressed during weekly project team meetings. A dedicated rehab coordinator served as a navigator and assisted patients to develop patient centered goals.This approach to planning services facilitated cross sector collaboration and innovation in community stroke rehabilitation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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; both teacher heads agree on what is shown here.
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".