ECE2023 Eposter Presentations Diabetes, Obesity, Metabolism and Nutrition (355 abstracts)
1Far Eastern Federal University, School of Medicine, Vladivostok, Russia; 2University of Birmingham, College of Medical and Dental Sciences, Birmingham, United Kingdom; 3Imperial College London, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, London, United Kingdom; 4University of Birmingham, Institute of Metabolism and Systems Research, Birmingham, United Kingdom; 5University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital, Birmingham, United Kingdom
Introduction: The global migration of healthcare professionals has enabled those trained in low-and-middle income countries (LMICs) to move and work in high-income countries (HICs) and vice-versa. While medical licensing exams in HICs are designed to assess competency of the immigrating HCPs, there is limited literature on the differences in approach to manage clinical cases between those from LMIC and HIC. Simulation via Instant Messaging Birmingham Advance (SIMBA) is a simulation-based training model using real-life clinical cases simulated via WhatsApp and Zoom. Participants from all over the world have been attending the sessions since its inception in 2020. In this study, we investigated if there was a difference in performance during SIMBA between participants depending on their country of residence.
Method: All participants who attended and completed both pre- and post-SIMBA surveys in 17 SIMBA sessions from 2020 to 2022 were included in this study. Participants were grouped into those from LMICs and HICs according to their country of residence based on the 2022 World Bank report. Participants performance was assessed using the Global Rating Scale (GRS) adapted for each session, which scored the participant on a scale of 1 (poor) to 5 (excellent) in 6 domains: History Taking, Physical Examination, Investigations, Result Interpretation, Clinical Judgement, and Management. The mean GRS domain scores were calculated using multiple linear regression models, adjusting for sex, country, training, and number of WhatsApp messages. Mean difference between performance scores was calculated using a paired t-test.
Results: A total 281 participants ((HICs 191 (67.9%), LMICs 90 (32.0%)) from 49 countries ((HICs 18(36.7%), LMICs 31(63.2%)) were included in this analysis. Participants from LMICs scored lower in History Taking (LMICs: 3.5 vs HICs: 3.8; P=0.0117), Investigations (LMICs: 3.1 vs HICs: 3.6; P=<0.0001), Clinical Judgement (LMICs: 2.9 vs HICs: 3.4; p =<0.0001), and Management (LMICs: 2.3 vs HICs: 2.9; P=<0.0001). The performance was similar for participants across all countries in Physical Examination (LMICs: 3.5 vs HICs: 3.6; P=0.2335) and Result Interpretation (LMICs: 2.6 vs HICs: 2.8; P=0.3264).
Conclusion: There is a difference in performance of solving clinical cases between participants from LMICs and HICs which may reflect the difference in clinical training in their local region. Future research whether serial simulation-based learning can reduce this difference is currently underway.