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Co-creation and delivery of a simulation-based learning model for locally employed doctors: Findings from the SIMBA-LED study

Year: 2025
Type: article
Abstract: BACKGROUND AND OBJECTIVES: Locally employed doctors (LEDs) form a substantial part of the healthcare workforce, but often lack structured educational support. Simulation via Instant Messaging for Bedside Application (SIMBA) is a simulation-based learning model that has been shown to enhance confidence in clinical practice. This study developed and assessed a tailored SIMBA programme for LEDs, focusing on acute medical scenarios. DESIGN AND SETTING: A serial cross-sectional study was conducted (July 2023-January 2024) using Kern's six-step framework. The programme was delivered online via mobile and computer platforms. PARTICIPANTS AND INTERVENTIONS: Forty-six LEDs participated. Stakeholder and participant interviews informed an LED-specific intervention, delivered through 11 SIMBA sessions. Participants completed pre- and post-session surveys assessing confidence and the acceptability of SIMBA, including six Accreditation Council for Graduate Medical Education (ACGME) core competencies. MAIN OUTCOME MEASURES: Thematic analysis of interviews assessed acceptability. Changes in self-reported confidence were analysed using the Wilcoxon signed-rank test. RESULTS: Stakeholders highlighted that LEDs stabilise the workforce but receive less teaching support than HEE trainees. SIMBA had high acceptability (100% found cases applicable; 87% preferred SIMBA over traditional learning). Confidence increased by 32.4%, with greater improvements in simulated (46.2% vs 84.8%, p < 0.0001) than non-simulated (41.5% vs 66.5%, p < 0.0001) scenarios. Notable improvements were in patient management (84.8%), patient care (71.7%), and practice-based learning (69.6%). CONCLUSIONS: A tailored SIMBA programme significantly enhances LEDs' confidence in acute medical scenarios and is highly acceptable. Integrating SIMBA into LED training may improve professional development and patient care.
Authors: Swetha Palanichamy, Kashish Malhotra, Yun S Sin, Nevil C Philip, Amynta Arshad, Harshin Balakrishnan, Haneesh Johal, Saima Kauser-Malik, Sahrish Khan, Shreyas Moodalbyle, Maiar Elhariry, Lakshmi Narayanan, Punith Kempegowda, Catherine Atkin, Lavanya Pelluri, Pavithra Sakthivel, Ella Blendis, Ganesh Gowda, Anisah Ali, Minerva Arshad, Pranav Vishwanath Iyer, Rocco Sheldon, Yasmin Abdulkadir Ali Farah, Maariyah Vankad, Advaiy Suburamu
Language: English
Subfield: Physiology
Field: Medicine
Open Access status: gold
Estimated APC paid: $2,500
OpenAlex ID: W4415516964
FWCI:
Cites: 14
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