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

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.