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Computers in Human Behavior
Article . 2015 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
DBLP
Article . 2015
Data sources: DBLP
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Understanding empathy training with virtual patients

Authors: Andrea Kleinsmith; Diego J. Rivera-Gutierrez; Glen Finney; Juan Cendan; Benjamin Lok;

Understanding empathy training with virtual patients

Abstract

While the use of virtual characters in medical education is becoming more and more commonplace, an understanding of the role they can play in empathetic communication skills training is still lacking. This paper presents a study aimed at building this understanding by determining if students can respond to a virtual patient's statement of concern with an empathetic response. A user study was conducted at the [blinded] College of Medicine in which early stage medical students interacted with virtual patients in one session and real humans trained to portray real patients (i.e., standardized patients) in a separate session about a week apart. During the interactions, the virtual and 'real' patients presented the students with empathetic opportunities which were later rated by outside observers. The results of pairwise comparisons indicate that empathetic responses made to virtual patients were rated as significantly more empathetic than responses made to standardized patients. Even though virtual patients may be perceived as artificial, the educational benefit of employing them for training medical students' empathetic communications skills is that virtual patients offer a low pressure interaction which allows students to reflect on their responses.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
127
Top 1%
Top 10%
Top 10%
bronze