Implementing Strengths-Based, Positive Approaches Within Rheumatology Fellowship Training: A Realist Evaluation of...

Area of Science:

• Medical Education Research
• Organizational Psychology
• Healthcare Leadership

Background:

• Appreciative Inquiry (AI) is a strengths-based framework for organizational change and engagement.
• Its application in graduate medical education shows promise, yet specific mechanisms driving educational outcomes remain unclear.
• This study focuses on a rheumatology fellowship program to explore AI's impact.

Purpose of the Study:

• To examine the causal relationships between contexts, mechanisms, and outcomes of AI-based interventions in a rheumatology fellowship.
• To develop a realist evaluation-informed program theory for AI implementation.
• To generate actionable recommendations for leaders in rheumatology fellowship programs.

Main Methods:

• A three-phase realist evaluation approach was employed.
• Phase 1: Scoping review to develop an initial program theory.
• Phase 2: Realist interviews to refine context-mechanism-outcome configurations and finalize the program theory.
• Phase 3: Utilization of the final program theory to formulate implementation recommendations.

Main Results:

• The final program theory detailed 15 contexts, 10 mechanisms, and 10 outcomes, forming 43 context-mechanism-outcome configurations.
• Key recommendations include establishing permission structures for strengths-based feedback, consistent application of AI principles across program levels, and co-designing virtuous cycles for sustainability.
• Analysis revealed intricate causal pathways influencing educational outcomes.

Conclusions:

• A theory has been generated on implementing Appreciative Inquiry in rheumatology fellowships to improve educational outcomes.
• Leaders should tailor AI interventions to the specific context of their training programs due to complex causal relationships.
• The findings provide a framework for optimizing AI's effectiveness in medical education settings.