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Examining Accuracy of Self-Assessment of In-Training Examination Performance in a Context of Guided Self-Assessment
Oksana Babenko1, Denise Campbell-Scherer, Shirley Schipper
1Department of Family Medicine, University of Alberta.
Background And Objectives:
In our family medicine residency program, we have established a culture of guided self-assessment through a systematic approach of direct observation of residents and documentation of formative feedback. We have observed that our residents have become more accurate in self-assessing their clinical performance. The objective of this study was to examine whether this improved accuracy extended to residents' self-assessment of their medical knowledge and clinical reasoning on the In-Training Examination (ITE).
Methods:
In November each year, residents in their first (PGY1) and second (PGY2) years of residency take the ITE (240 multiple-choice questions). Immediately before and right after taking the ITE, residents complete a questionnaire, self-assessing their knowledge and predicting their performances, overall and in eight high-level domains. Consented data from residents who took the ITE in 2009-2015 (n=380, 60% participation rate) were used in the Generalized Estimating Equations analyses.
Results:
PGY2 residents outperformed PGY1 residents; Canadian medical graduates consistently outperformed international medical graduates; urban and rural residents performed similarly overall. Residents' pre-post self-assessments were in line with residents' actual performance on the overall examination and in the domains of Adult Medicine and Care of Surgical Patients. The underperforming residents in this study accurately predicted both pre- and post-ITE that they would perform poorly.
Conclusion:
Our findings suggest that the ITE operates well in our program. There was a tendency among residents in this study to appropriately adjust their self-assessment of their overall performance after completing the ITE. Irrespective of the residency year, resident self-assessment was less accurate on individual domains.
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