Scoping Review: Interprofessional Simulation as an Effective Modality to Teaching Interprofessional Collaborative Competencies in the Emergency Department
Bibliographic record
Abstract
Background: A scoping review was conducted to map the current body of research pertaining to simulation-enhanced interprofessional education (Sim-IPE) as a modality for teaching interprofessional collaboration (IPC) in the emergencydepartment (ED). Methods and Findings: The research team followed the PRISMA Extension for Scoping Reviews framework. Studies were included if they involved two or more healthcare professions, utilized simulation as the learning method for interprofessional education (IPE), involved simulation pertaining to the ED, and identified at least one Canadian Interprofessional Health Collaborative or Interprofessional Education Collaborative IPC competency as a learning outcome. In total, 896 studies were included for title and abstract screening and 806 were deemed irrelevant. Ninety full-text studies were assessed for eligibility and 34 were included in the review. Conclusions: Eighteen studies found Sim-IPE to be an effective method for teaching interprofessional competencies in the ED. Simulation-enhanced interprofessional education appears to be a promising methodology for teaching IPC competencies to ED healthcare professionals. Interprofessional collaboration competency frameworks should be utilized to guide Sim-IPE, and assessment tools specific to interprofessional competencies should be used in the assessment phase of Sim-IPE. Faculty development is a crucial component of Sim-IPE. Further longitudinal and outcome-based research is required.
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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 | 0.036 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".