Mary Yaden1, David Yaden2, Anneke Buffone3, Johannes Eichstaedt3, Patrick Crutchley3, Laura Smith3, Jonathan Cass4, Clara Callahan4, Susan Rosenthal4, Lyle Ungar5, Andrew Schwartz6, Mohammadreza Hojat4. 1. Department of Psychiatry at the University of Pennsylvania, Philadelphia, PA, USA. 2. Department of Psychiatry and Behavioral Sciences at Johns Hopkins Medicine, Baltimore, MD, USA. 3. Department of Psychology at the University of Pennsylvania, Philadelphia, PA, USA. 4. Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA, USA. 5. Department of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA, USA. 6. Department of Computer Science at Stony Brook University, Stony Brook, New York, USA.
Abstract
OBJECTIVES: This study aimed to determine whether words used in medical school admissions essays can predict physician empathy. METHODS: A computational form of linguistic analysis was used for the content analysis of medical school admissions essays. Words in medical school admissions essays were computationally grouped into 20 'topics' which were then correlated with scores on the Jefferson Scale of Empathy. The study sample included 1,805 matriculants (between 2008-2015) at a single medical college in the North East of the United States who wrote an admissions essay and completed the Jefferson Scale of Empathy at matriculation. RESULTS: After correcting for multiple comparisons and controlling for gender, the Jefferson Scale of Empathy scores significantly correlated with a linguistic topic (r = .074, p < .05). This topic was comprised of specific words used in essays such as "understanding," "compassion," "empathy," "feeling," and "trust." These words are related to themes emphasized in both theoretical writing and empirical studies on physician empathy. CONCLUSIONS: This study demonstrates that physician empathy can be predicted from medical school admission essays. The implications of this methodological capability, i.e. to quantitatively associate linguistic features or words with psychometric outcomes, bears on the future of medical education research and admissions. In particular, these findings suggest that those responsible for medical school admissions could identify more empathetic applicants based on the language of their application essays.
OBJECTIVES: This study aimed to determine whether words used in medical school admissions essays can predict physician empathy. METHODS: A computational form of linguistic analysis was used for the content analysis of medical school admissions essays. Words in medical school admissions essays were computationally grouped into 20 'topics' which were then correlated with scores on the Jefferson Scale of Empathy. The study sample included 1,805 matriculants (between 2008-2015) at a single medical college in the North East of the United States who wrote an admissions essay and completed the Jefferson Scale of Empathy at matriculation. RESULTS: After correcting for multiple comparisons and controlling for gender, the Jefferson Scale of Empathy scores significantly correlated with a linguistic topic (r = .074, p < .05). This topic was comprised of specific words used in essays such as "understanding," "compassion," "empathy," "feeling," and "trust." These words are related to themes emphasized in both theoretical writing and empirical studies on physician empathy. CONCLUSIONS: This study demonstrates that physician empathy can be predicted from medical school admission essays. The implications of this methodological capability, i.e. to quantitatively associate linguistic features or words with psychometric outcomes, bears on the future of medical education research and admissions. In particular, these findings suggest that those responsible for medical school admissions could identify more empathetic applicants based on the language of their application essays.
Entities:
Keywords:
admission; empathy; linguistic analysis; medical education; patient-centered care
Authors: H Andrew Schwartz; Johannes C Eichstaedt; Margaret L Kern; Lukasz Dziurzynski; Stephanie M Ramones; Megha Agrawal; Achal Shah; Michal Kosinski; David Stillwell; Martin E P Seligman; Lyle H Ungar Journal: PLoS One Date: 2013-09-25 Impact factor: 3.240