title: Exploratory and confirmatory factor analysis of medical professionalism core attributes scale.
abstract: OBJECTIVE
  Evaluating the professional attributes of medical students is critical, because medical professionalism is an essential quality of a good doctor. But, few studies have examined the tools for assessing such attributes. This study analyzed factors of medical professionalism in medical students to develop standards that can assess medical professional attributes.
  
  
  METHODS
  A total of 1,508 medical students in Korean medical schools or colleges answered a self-assessment survey of medical professionalism elements from 2005 to 2012 that we developed. The survey consisted of core 31 attributes on a 5-point Likert scale. Factor analysis was performed using SPSS version 20.0 and AMOS version 20.0.
  
  
  RESULTS
  Exploratory factor analysis revealed six factors with total variance of 59.56%. The factors were termed 'empathy and accountability,' 'self-development skills,' 'academic competence,' 'interpersonal skills,' 'high intelligence,' and 'attitude towards oneself and life.' These factors showed statistically significant correlation (0.310~0.663). From the confirmatory factor analysis a six-factor model were appropriate (CFI=0.873, TLI=0.853, RMSEA=0.065). Cronbach-alpha of six factors ranged from 0.718 to 0.864.
  
  
  CONCLUSIONS
  Good doctors need to have not only appropriate standards of medical knowledge but also skills to understand and communicate well with patients, as well as self-management skills, which should not be overlooked in the medical education curriculum. By optimizing the results of this study, a more refined assessment tool of professionalism can be exploited.
authors: Department of Medical Education, Konyang University College of Medicine, Daejeon, Korea.
authors: Keumho
authors: K
authors: Lee
authors: Department of Medical Education, Konyang University College of Medicine, Daejeon, Korea.
authors: Yera
authors: Y
authors: Hur
keywords: Medical professionalism; Medical students; Statistical factor analysis