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

Area of Science:

• Medical Education
• Simulation-Based Learning
• Healthcare Workforce Development

Background:

• Locally employed doctors (LEDs) are crucial to healthcare but often lack structured educational support.
• Simulation via Instant Messaging for Bedside Application (SIMBA) is a validated model for enhancing clinical confidence.
• This study focused on adapting SIMBA for LEDs in acute medical settings.

Purpose of the Study:

• To develop and assess a SIMBA program specifically tailored for locally employed doctors (LEDs).
• To evaluate the impact of the tailored SIMBA program on LEDs' confidence in acute medical scenarios.
• To determine the acceptability and effectiveness of SIMBA for LED professional development.

Main Methods:

• A serial cross-sectional study design was employed over seven months (July 2023-January 2024).
• The intervention involved 11 online SIMBA sessions, informed by stakeholder and participant interviews.
• Pre- and post-session surveys assessed confidence and acceptability, with thematic analysis of interviews and Wilcoxon signed-rank tests for quantitative data.

Main Results:

• SIMBA demonstrated high acceptability, with 100% finding cases applicable and 87% preferring it over traditional methods.
• Participant confidence increased by 32.4% overall, with significant gains in both simulated (46.2% to 84.8%) and non-simulated (41.5% to 66.5%) scenarios.
• Key areas of improvement included patient management (84.8%), patient care (71.7%), and practice-based learning (69.6%).

Conclusions:

• A tailored SIMBA program effectively enhances LEDs' confidence and competence in acute medical situations.
• The SIMBA program is highly acceptable to LEDs, offering a valuable tool for their professional growth.
• Integrating SIMBA into LED training pathways can lead to improved healthcare delivery and patient outcomes.