Supporting Integrated Stroke Care Transitions: An Interprofessional Learning Simulation
Bibliographic record
Abstract
Introduction: Older adults living with stroke and other comorbidities often experience care transitions across multiple health sectors. Multiple transitions jeopardize safe patient care. Managing stroke in addition to other comorbidities requires the expertise of multiple health and social care providers. Implementing best practices for integrated stroke care is critical to ensuring patients receive quality care to support full community reintegration. Interprofessional stroke-specific teams are required to deliver the specialized care required. Our team has developed a unique simulation that focuses on enhancing competencies for interprofessional integrated stroke care to support care quality and patient safety. Description: Guided by the INACSL Standards of Best Practice for simulation development,researchers and expert stroke clinicians co-designed the simulation scenario. Learning objectives were informed by experiential and reflective learning theories, and theCanadian Patient Safety Institute (CPSI) Safety Competencies. Multiple types of fidelity (e.g., physical environment, conceptual, psychological) were incorporated tocreate a realistic case scenario representing current best practices for stroke and care transitions. The simulation is intentionally focused on managing an older stroke survivor’s complex trajectory through two formal integrated care transitions from hospital to home in the community. The simulation incorporates concepts related to current system-level changes andexisting integrated models of stroke care in Ontario, Canada. Integrated care models are people-centered approaches to address fragmented care systems to improve quality of care, through the coordination of people’s care needs across services, providers, and settings. The simulation promotes active learning, problem-solving, and critical thinking skills. The content incorporates Canadian Best Practices for Stroke Care, CPSI Safety Competencies for Health Professionals, the International Foundation of Integrated Care Pillars, and the Model for Improvement quality framework. Discussion: This novel open-access simulation consists of two video-recorded scenes featuring an interprofessional integrated approach to stroke care across two care transitions from 1) acute care to a rehabilitation hospital, and 2) a rehabilitation hospital back to the patient’s home in the community. The simulation profiles the specific knowledge and skills of the interprofessional team members’ roles for stroke care. Further, the simulation intentionally highlights how the patient is actively engaged as a member of the interprofessional integrated stroke team. The video simulation provides an opportunity for use in the context of undergraduate/graduate courses with further uptake that can be considered in practice contexts such as stroke rehabilitation programs to enhance safe, quality integrated care transitions. Results from the in-class evaluation of the video simulation focusing on the student experiences of the debrief discussion will be presented. Next steps: We aim to engage clinicians in additional practice partner agencies from the hospital and community sector to support workforce capacity for integrated stroke care.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".