SIMBA: using Kolb's learning theory in simulation-based learning to improve participants' confidence.

Abstract

BACKGROUND Simulation via Instant Messaging- Birmingham Advance (SIMBA) delivers simulation-based learning (SBL) through WhatsApp® and Zoom® based on Kolb's experiential learning theory. This study describes how Kolb's theory was implemented in practice during SIMBA adrenal session. METHODS SIMBA adrenal session was conducted for healthcare professionals and replicated Kolb's 4-stage cycle: (a) concrete experience-online simulation of real-life clinical scenarios, (b) reflective observation-discussion and Q&A following simulation, (c) abstract conceptualisation-post-session MCQs, and (d) active experimentation-intentions to implement the acquired knowledge in future practice. Participants' self-reported confidence levels for simulated and non-simulated cases pre- and post-SIMBA were analysed using Wilcoxon Signed-Rank test. Key takeaway and feedback were assessed quantitatively and qualitatively in a thematic analysis. RESULTS Thirty-three participants were included in the analysis. A Wilcoxon signed-rank test showed that the SIMBA session elicited a statistically significant change in participants' self-reported confidence in their approach to Cushing's syndrome (Z = 3.873, p = 0.0001) and adrenocortical carcinoma (Z = 3.970, p < 0.0001). 93.9% (n = 31/33) and 84.8% (n = 28/33) strongly agreed/agreed the topics were applicable to their clinical practice and accommodated their personal learning style, respectively. 81.8% (n = 27/33) reported increase in knowledge on patient management, and 75.8% (n = 25/33) anticipated implementing learning points in their practice. CONCLUSIONS SIMBA effectively adopts Kolb's theory to provide best possible experience to learners, highlighting the advantages of utilising social media platforms for SBL in medical education. The ability to conduct SIMBA sessions at modest cost internationally paves way to engage more healthcare professionals worldwide

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Last time updated on 24/04/2022

This paper was published in HEFT Repository.

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