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Record W4413358087 · doi:10.5334/ijic.nacic24162

Using an Interprofessional Learning Simulation to Support Integrated Stroke Care Transitions

2025· article· en· W4413358087 on OpenAlexaboutno aff
Sherry Espin

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsIntegrated careStroke (engine)NursingHealth careMedicinePsychologyComputer scienceMedical educationProcess managementKnowledge managementBusinessEngineeringPolitical science

Abstract

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Background: Older adults living with stroke and other comorbidities often experience care transitions across multiple health sectors. Managing stroke in addition to other comorbidities requires the expertise of an interprofessional stroke-specific team. A learning simulation tool can develop competencies for interprofessional integrated stroke care to support care quality and patient safety. Findings from this work are relevant to faculty/educators in health professions education programs and for clinicians in stroke care settings. The simulation tool is freely accessible and can be adapted to the educational context for use with interprofessional stroke teams or to use as an interprofessional education activity. Approach: Guided by the INACSL Standards of Best Practice for simulation development, researchers and expert stroke clinicians co-designed the simulation scenario. We engaged a team of researchers and interprofessional front-line stroke clinicians from an academic teaching hospital in designing the simulation case scenarios to ensure the relevancy to current practice. This included developing the script for the video scenes which included an actor patient role. The case incorporated both stroke patient and family caregiver perspectives.Learning objectives were informed by experiential and reflective learning theories, and the Canadian Patient Safety Institute (CPSI) Safety Competencies. Multiple types of fidelity (e.g., physical environment, conceptual, psychological) were incorporated to create a realistic case scenario representing current best practices for stroke and care transitions. The simulation is intentionally focused on managing an older stroke survivor 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 and existing 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 care needs across services, providers, andsettings. 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. Results: This innovative open-access simulation features two video-recorded scenes featuring an interprofessional integrated approach to stroke care across two care transitions from ) acute care to a rehabilitation hospital, and 2) a rehabilitation hospital back to the patient 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 has been used 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 discussions will be presented. Implications: This simulation can be used in a variety of contexts to support learning about interprofessional integrated stroke care in both academic and practice settings. The series of debriefing questions can be adapted for use within specific contexts. To date, there has been some uptake in hospital stroke teams. Next Steps: We are currently building on this simulation to co-design and experiential learning initiative to inform further content on community-based integrated stroke care. In this work we are engaging stroke community members including health and social care providers, stroke patients, family caregivers, undergraduate and graduate students in co-developing the content and pedagogical approaches.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.022
GPT teacher head0.378
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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