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 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 | 0.033 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".