A31 Reimagining Clinical Education: Building a Multimodal Simulation Program to Advance Clinical Readiness in Undergraduate Nursing
Bibliographic record
Abstract
Introduction: Across the globe, healthcare systems are experiencing rapid transformation, driven by advances in technology, increasing patient acuity, and evolving professional standards [1]. These shifts have elevated the expectations placed on newly graduated nurses, particularly in their ability to demonstrate critical thinking and clinical judgment. This shift has highlighted the need for innovation in clinical education. Recognizing these challenges, a Faculty of Nursing in Canada saw the opportunity to fundamentally reimagine its approach to clinical education through the intentional development and implementation of an innovative simulation program. The goal was to transition from sparse, ad hoc use of simulation to the comprehensive use of high-quality multimodal simulation to promote clinical judgment and critical thinking. Methods: A simulation program was developed and integrated across the undergraduate nursing curriculum. The program incorporates three simulation modalities: in-person simulation, immersive virtual reality simulation, and screen-based virtual simulation. Informed by the International Nursing Association for Clinical Simulation and Learning’s Healthcare Standards of Best Practice [2], the design is grounded in progressive complexity, aiming to create coherent and scaffolded learning experiences. The faculty engaged in detailed curriculum planning to support the development of consistent simulation experiences throughout all program years. Collaboration and iterative feedback informed implementation. Results: The completed program provides students with over 100 simulation experiences throughout the undergraduate curriculum. These simulations expose learners to diverse clinical contexts mirroring global health priorities and challenges. The program’s standardized design has fostered faculty development and enhanced alignment across courses, promoting a more cohesive and integrated clinical curriculum. Discussion: This initiative offers a replicable model for institutions seeking to modernize nursing education and better prepare students for the complexities of contemporary healthcare. The deliberate integration of multimodal simulation into the undergraduate nursing curriculum has transformed clinical education at this institution. Ethics Statement: As the submitting author, I can confirm that all relevant ethical standards of research and dissemination have been met. Additionally, I can confirm that the necessary ethical approval has been obtained, where applicable References 1. Gordon R, Riley J, Dupont D, Rogers B, Witherspoon R, Day K, Horsley E, Killam L. Facilitator development for pre-registration health professions simulation: A scoping review protocol. JBI Evid Synth. 2025;23(4):812–21. doi: 10.11124/JBIES-24-00130. 2. Watts PI, Rossler K, Bowler F, Miller C, Charnetski M, Decker S, Molloy M, Persico L, McMahon E, McDermott D, Hallmark B. Onward and Upward: Introducing the Healthcare Simulation Standards of Best Practice. Clin Sim Nurs. 2021;58:1–4. doi: 10.1016/j.ecns.2021.08.006.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".