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Determining Component Weights in a Communications Assessment Using Judgmental Policy Capturing
Leo M Harvill1, F Forrest Lang1, Ronald S McCord
1a Departments of Medical Education and Family Medicine James H. Quillen College of Medicine East Tennessee State University.
Judgmental policy capturing (JPC) effectively weights medical education assessments. Experts prioritized active listening and patient rapport, showing diverse component importance in performance evaluations.
Area of Science:
- Medical Education
- Psychometrics
- Healthcare Communication Assessment
Background:
- Accurate weighting of performance assessment components is crucial in medical education.
- Judgmental policy capturing (JPC) offers a statistical method to understand expert decision-making strategies.
- The feasibility of JPC for defining weights in complex assessments needed investigation.
Purpose of the Study:
- To investigate the feasibility of using judgmental policy capturing (JPC) for determining appropriate weights for performance assessment components in medical education.
- To establish statistically derived weights for communication skills assessed in medical students.
Main Methods:
- Iterative judgmental policy capturing (JPC) was employed.
- A panel of four communication skill experts evaluated six core communication skill scores.
- Scores were derived from a communications objective structured clinical examination (OSCE) for medical students.
Main Results:
- Experts assigned varying importance to different communication skills.
- Information management received the lowest weight (8.5%).
- Rapport building (15.8%), agenda setting (15.4%), and addressing feelings (14.1%) received moderate, similar weights.
- Active listening (20.1%) and reaching common ground (25.5%) were weighted highest.
Conclusions:
- Judgmental policy capturing (JPC) is an effective method for determining weights in complex clinical assessment components.
- The derived weights can differ significantly from initial assumptions or traditional scoring methods.
- This approach provides a data-driven basis for refining assessment tools in medical education.
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