Delivering Bad News in Clinical Performance Examinations: Medical Students' Delivery of an Advanced Cancer Diagnosis and Standardized Patients' Responses.
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This paper examines how medical students deliver diagnostic information in the context of bad news and how standardized patients (individuals trained to portray patient roles based on case scenarios)
APA
Park SH, Kim CW, et al. (2025). Delivering Bad News in Clinical Performance Examinations: Medical Students' Delivery of an Advanced Cancer Diagnosis and Standardized Patients' Responses.. Health communication, 1-11. https://doi.org/10.1080/10410236.2025.2560033
MLA
Park SH, et al.. "Delivering Bad News in Clinical Performance Examinations: Medical Students' Delivery of an Advanced Cancer Diagnosis and Standardized Patients' Responses.." Health communication, 2025, pp. 1-11.
PMID
41020328
Abstract
This paper examines how medical students deliver diagnostic information in the context of bad news and how standardized patients (individuals trained to portray patient roles based on case scenarios) respond. Using conversation analysis, we analyze 82 video-recorded consultations from a clinical performance examination at a university in Korea. We identify two distinct ways in which medical students deliver a stage IV colon cancer diagnosis. First, students may name the diagnosis without explicating the underlying evidence, simply referring to prior tests as the basis for the diagnosis. Second, students may explicate the evidence before naming the diagnosis, delivering the information in a stepwise manner. Standardized patients often problematize the first delivery method, treating the absence of evidence explication as "missing" - even though their scenario does not require them to do so. In contrast, they do not criticize the second method. This variation in standardized patients' responses can help identify aspects of students' delivery practices that warrant further reflection. The findings offer insights for improving communication training in bad news delivery and extend conversation analytic research on simulated interactions.
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