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Scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices (Eccles & Mittman, 2006).
The field of simulation-based education has moved through phases of descriptive scholarship, justification, and verification research, with the ultimate goal of generating evidence of the most effective and efficient use of simulation. We need models to put this evidence into practice (Dubrowski et al., 2018).
Simulation plays a crucial role in improving the quality of education in health professions. However, the success observed in controlled experimental settings during the development and testing of simulation programs does not automatically translate into improved learner outcomes when these programs are implemented in the real world. Programs built on available theory and evidence may perform well in experimental settings, but their outcomes often fall short of expectations in practical applications. This discrepancy is attributed to the success of a program being dependent on two factors:
1. The quality of the program itself
2. The effectiveness of its implementation
The second factor is frequently overlooked in the realm of simulation-based health professions education. Despite the emergence of implementation science, a field guiding the application of evidence-based programs in various contexts over the past two decades, literature on simulation in healthcare indicates limited integration.
Implementation science involves the rigorous study of methods that facilitate the systematic adoption of research findings and other evidence-based practices. Its overarching objective is to provide an evidence-based approach to delivering programs in practice, aiming to achieve specific health and education outcomes while maximizing the return on research investments. The primary challenge lies in the need for essential attention to both program quality and implementation quality to achieve the intended outcomes. Simply having a well-designed simulation program does not guarantee the realization of the intended learning outcomes.
This aspect of maxSIMhealth's research activities aims to offer user-friendly tools to bridge the existing gap between research and practice in simulation-based health professions education. In collaboration with the Copenhagen Academy for Medical Education and Simulation (CAMES), we are developing, validating, and applying evidence-based implementation models for use in simulation programming.