A metacognitive confidence calibration (MCC) tool to help medical students scaffold diagnostic reasoning in decision-making during high-fidelity patient simulations

Abstract

This study demonstrates the feasibility of a metacognitive confidence calibration tool (MCC) to assess and promote novices in the learning of diagnostic reasoning and treatment decisions on patient care in real time during high-fidelity patient simulations while comparing confidence and accuracy data and identifying students’ scientific misconceptions. Results revealed the presence of overconfidence bias, overtreatment, and the misconception of metabolic acidosis as the cause of the patient’s problems rather than a consequence of untreated diabetes mellitus.