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In-training evaluations: developing an automated screening tool to measure report quality.
Ramprasad Bismil1, Nancy L Dudek, Timothy J Wood
1Department of Psychiatry, University of Ottawa, Ottawa, Ontario, Canada.
Medical Education
|June 10, 2014
Summary
A new screening measure, Proxy-CCERR, accurately predicts the quality of In-Training Evaluation Reports (ITERs). This less resource-intensive method allows for broader feedback on ITER training programs.
Area of Science:
- Medical Education
- Assessment and Evaluation
Background:
- In-training evaluation (ITE) is crucial for assessing resident competencies.
- The quality of In-Training Evaluation Reports (ITERs) can be variable, necessitating training programs.
- The Completed Clinical Evaluation Report Rating (CCERR) reliably assesses ITER quality but is resource-intensive.
Purpose of the Study:
- To develop a less resource-intensive screening measure, Proxy-CCERR, to predict CCERR scores.
- To enable more efficient evaluation of ITER training programs.
Main Methods:
- Multiple regression analysis was used to develop a predictive model.
- The model was created using a dataset of 269 ITERs and validated on 300 additional ITERs.
Main Results:
- A three-variable model accurately predicted CCERR scores (R² = 0.61).
- Key predictors included comment word count, rating variability, and completion rate of comment boxes.
Conclusions:
- ITER quality, as measured by CCERR, can be predicted efficiently.
- The Proxy-CCERR model's variables are easily automated, facilitating feedback for large groups.
- This enables automated feedback systems for ITER training programs.
