Presentation Description
Sumaiya Adam1
Yvette Hlophe1 and Vanessa Burch2
1 University of Pretoria
2 Colleges of Medicine of South Africa
Yvette Hlophe1 and Vanessa Burch2
1 University of Pretoria
2 Colleges of Medicine of South Africa
Introduction:
CMSA aims to implement WBA for postgraduate medical training by 2025. EPAs define the knowledge, skills and attitudes expected from specialists, thereby facilitating training and assessment. Whilst WBA with an EPA framework has been implemented in various countries, there is scant data on experiences in resource-limited settings, with limited availability of HPE experts, who then depend on busy discipline-specific experts for development and implementation.
CMSA aims to implement WBA for postgraduate medical training by 2025. EPAs define the knowledge, skills and attitudes expected from specialists, thereby facilitating training and assessment. Whilst WBA with an EPA framework has been implemented in various countries, there is scant data on experiences in resource-limited settings, with limited availability of HPE experts, who then depend on busy discipline-specific experts for development and implementation.
Purpose:
To define EPAs for ObGyn postgraduate training, quantify the importance of each EPA, and explore the role of ChatGPT4 in defining competencies for each EPA.
To define EPAs for ObGyn postgraduate training, quantify the importance of each EPA, and explore the role of ChatGPT4 in defining competencies for each EPA.
Methodology:
Individual EPAs were defined using Backward Design to describe the Day 1 specialist competencies. Two rounds of modified Delphi and in-person workshop were performed to obtain consensus on core EPAs. The detailed competencies were defined per EPA and was agreed by further Delphi surveys. The use of ChatGPT4 was explored to define the core activities and writing detailed EPAs.
Individual EPAs were defined using Backward Design to describe the Day 1 specialist competencies. Two rounds of modified Delphi and in-person workshop were performed to obtain consensus on core EPAs. The detailed competencies were defined per EPA and was agreed by further Delphi surveys. The use of ChatGPT4 was explored to define the core activities and writing detailed EPAs.
Results:
Item analysis yielded 10 core EPAs of the 30 EPAs defined, with a survey response of 8/10 (80%) and 6/10 (60%) following the first two Delphis. The in-person workshop resulted in unanimous agreement that the 10 core EPAs were "absolutely essential", 15 were "moderately important" and 5 were "nice to have". Experts agreed that a stepwise increase in the level of competence was required dependent on the stage of training. The second set of Delphis had a response of 3/10 (30%) and 1/10 (1%). ChatGPT4 showed a positive correlation with the EPAs that defined the specialty, as well as the defined competencies for each of the selected EPAs.
Item analysis yielded 10 core EPAs of the 30 EPAs defined, with a survey response of 8/10 (80%) and 6/10 (60%) following the first two Delphis. The in-person workshop resulted in unanimous agreement that the 10 core EPAs were "absolutely essential", 15 were "moderately important" and 5 were "nice to have". Experts agreed that a stepwise increase in the level of competence was required dependent on the stage of training. The second set of Delphis had a response of 3/10 (30%) and 1/10 (1%). ChatGPT4 showed a positive correlation with the EPAs that defined the specialty, as well as the defined competencies for each of the selected EPAs.
Conclusion:
WBA requires EPAs and benchmarks for each stage of training. ChatGPT4, by harnessing available knowledge, is a viable tool to define EPAs for a specialty thereby expediting the process of implementation of WBA, especially in resource-rich and resource- limited environments.
WBA requires EPAs and benchmarks for each stage of training. ChatGPT4, by harnessing available knowledge, is a viable tool to define EPAs for a specialty thereby expediting the process of implementation of WBA, especially in resource-rich and resource- limited environments.
References (maximum three)
1. Caccia, N., Nakajima, A., Scheele, F., & Kent, N. (2015). Competency-Based Medical Education: Developing a Framework for Obstetrics and Gynaecology. Journal of obstetrics and gynaecology Canada : JOGC = Journal d'obstetrique et gynecologie du Canada : JOGC, 37(12), 1104–1112. https://doi.org/10.1016/s1701-2163(16)30076-7
2. Garofalo, M., & Aggarwal, R. (2018). Obstetrics and Gynecology Modified Delphi Survey for Entrustable Professional Activities: Quantification of Importance, Benchmark Levels, and Roles in Simulation-based Training and Assessment. Cureus, 10(7), e3051. https://doi.org/10.7759/cureus.3051
3. ten Cate, Olle. (2018). A primer on entrustable professional activities. Korean Journal of Medical Education. 30. 1-10. 10.3946/kjme.2018.76.