Constructing Shared Mental Models in High-Acuity Clinical Trajectories: A Narrative Review and Proposed Framework for Multidisciplinary Simulation Integrating Pre-Hospital, Emergency, and Surgical Disciplines
Bibliographic record
Abstract
Background: High-acuity patient crises, particularly those involving complex surgical pathologies like bariatric or endocrine emergencies, demand seamless coordination across pre-hospital, emergency, and surgical teams. Traditional siloed training often fails to prepare these disparate groups for the intense collaboration required, leading to breakdowns in communication, role confusion, and delayed decision-making. Aim: This narrative review aims to synthesize current evidence on multidisciplinary simulation (MDS) as a pedagogical tool to build shared mental models, enhance interprofessional communication, and clarify role responsibilities among EMS, Emergency Medicine, Nursing, and General Surgery teams during time-sensitive events. Methods: A structured literature search was conducted across PubMed, CINAHL, Scopus, and Web of Science (2010-2024) using keywords related to simulation, interprofessional education, teamwork, and the specified clinical domains. Included literature focused on simulation involving at least three of the target disciplines in high-acuity settings. Results: The analysis reveals that MDS effectively improves non-technical skills, including situational awareness, closed-loop communication, and leadership. Scenario design principles emphasizing realism, cognitive fidelity, and structured debriefing are critical. Successful implementations, such as "Field-to-OR" or "Clinic-to-ICU" pathways for surgical complications, demonstrate improved clinical outcomes, including reduced time-to-intervention and enhanced team psychological safety. Conclusion: Multidisciplinary simulation is a powerful, evidence-based strategy for constructing the shared cognitive frames necessary for managing complex patient crises. To bridge persistent gaps in care continuity, healthcare institutions must prioritize and institutionalize immersive, cross-disciplinary simulation training that mirrors the high-stakes, interdependent nature of real-world emergency care.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".