Personality traits and emotion recognition abilities in two samples of Romanian medical students

Abstract

IntroductionPersonality traits and social cognition are important predictors for the future performance, success, adjustment and mental health of medical students.ObjectivesThe purpose of the study was to assess and compare personality traits and emotion recognition abilities of two medical student samples: general medicine (GM) and general nursing (GN) students.MethodsThe study was conducted between 2015–2016 on 173 Romanian medical undergraduates from the Timisoara “Victor Babes” university of medicine and pharmacy: sixth-year GM students (n = 96) and fourth-year GN students (n = 83). Personality traits were evaluated with the 60-item NEO five-factor inventory (NEO-FFI). Emotion recognition abilities were assessed with the revised version of the “reading the mind in the eyes” test.ResultsThe GM sample consisted of 31 (32.3%) males and 65 (67.7%) females, with a mean age of 24.65 years (SD = 1.81). The GN sample was comprised of 13 (15.7%) males and 70 (84.3%) females, with a mean age of 23.84 years (SD = 2.12). Overall, compared with female students, males gave considerably less correct answers in the “eyes test” (P = 0.002) and had lower mean scores in “agreeableness” (P = 0.005). GM students scored significantly higher than GN students in “neuroticism” (P = 0.01), obtained lower mean scores in “agreeableness” (P = 0.015) and had poorer emotion recognition abilities (P = 0.03) than their GN colleagues.ConclusionsGN students appear to be more emotionally stable and with better emotion recognition abilities than GM students. Results of the present study represent a starting point for other studies that may use larger and diverse student samples.Disclosure of interestThe authors have not supplied their declaration of competing interest.

A Recognition Method of Athletes’ Mental State in Sports Training Based on Support Vector Machine Model

“…Emotion recognition has been widely studied and applied in various fields. For example, emotion recognition is used in medical care [4][5][6], education [7][8][9], service industry [10][11][12], and other fields. For emotion recognition methods, there are mainly scale methods [13,14], machine learning [15][16][17], and deep learning algorithms [18][19][20].…”