Advancement of a Rehab Model of Care : The Designated Stroke Pod
Bibliographic record
Abstract
Advancement of a Rehab Model of Care: The Designated Stroke PodIn April 2015, Hotel Dieu Shaver Health and Rehabilitation Centre implemented a new rehab service delivery model, referred to as the POD Model. The goals of this transformation included: u2022tAdding value to the patient experienceu2022tAchieving better patient outcomesu2022tImproving program performance u2022tStrengthening interprofessional collaborationu2022tAdvancing best practices and expertiseSince implementation, Hotel Dieu Shaveru2019s key performance measures have shown positive results with; length of stay, Functional Independence MeasureTM efficiency, percentage of patients meeting their target length of stay and patient satisfaction scores. In 2017 a Stroke POD Planning Committee was formed to complete a program review and to further align our POD model with the Canadian Stroke Best Practice Guidelines. Three areas of opportunity were identified; timely access to rehabilitation, rehab intensity and further advancement of best practice care.In April 2018, following the recommendations of this committee, the PODs were re-configured to create one dedicated Stroke Pod. The changes included; u2022t Improving PT/OT to patient ratios to 1:7 u2022tDedicating a Physiatrist with stroke expertise u2022tDesignating these 7 Beds for ONLY Stroke Rehabilitation patientsu2022tTargeted patient schedulingThe indicators monitored through this proof of concept include;u2022tRehab Intensity timeu2022tPercentage of patients meeting their target length of stayu2022tNumber of days to access Stroke Rehabilitationu2022tAlignment to Canadian Stroke Best PracticesOur experience with the POD model and its evolution indicates that continuous quality improvement is multi-layered and involves the consideration of structure, processes, accountability and should be grounded by guiding principles.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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; 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".