Numerical Assessment Tool to Measure Realism in Clinical Simulation

Gleyvis Coro-Montanet (), María Jesús Pardo Monedero, Julia Sánchez Ituarte, Helena Wagner Porto Rocha and Carmen Gomar Sancho
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Gleyvis Coro-Montanet: Preclinical Dentistry Department, School of Biomedical and Health Sciences, Universidad Europea, 28670 Madrid, Spain
María Jesús Pardo Monedero: Preclinical Dentistry Department, School of Biomedical and Health Sciences, Universidad Europea, 28670 Madrid, Spain
Julia Sánchez Ituarte: Preclinical Dentistry Department, School of Biomedical and Health Sciences, Universidad Europea, 28670 Madrid, Spain
Helena Wagner Porto Rocha: Health Sciences Facilities, Universidad Europea, 28670 Madrid, Spain
Carmen Gomar Sancho: Researcher SIMLAB Group, Universidad Europea, 28670 Madrid, Spain

IJERPH, 2023, vol. 20, issue 3, 1-12

Abstract: Realism is indispensable in clinical simulation learning, and the objective of this work is to present to the scientific community the methodology behind a novel numerical and digital tool to objectively measure realism in clinical simulation. Indicators measuring accuracy and naturality constitute ProRealSim v.1.0 (Universidad Europea, Madrid, Spain) which allows the assessing of attained realism for three dimensions: simulated participant, scenography, and simulator. Twelve experts in simulation-based learning (SBL) analyzed the conceptual relevance of 73 initial qualitative indicators that were then reduced to 53 final indicators after a screening study evaluating eight medical clinical simulation scenarios. Inter- and intra-observer concordance, correlation, and internal consistency were calculated, and an exploratory factorial analysis was conducted. Realism units were weighted based on variability and its mathematical contribution to global and dimensional realism. A statistical significance of p < 0.05 was applied and internal consistency was significant in all cases (raw_alpha ≥ 0.9698094). ProRealSim v.1.0 is integrated into a bilingual, free, and open access digital platform, and the intention is to foster a culture of interpretation of realism for its better study and didactic use.

Keywords: simulation-based learning; high-fidelity simulation; realism; fidelity (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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