Prediction of emotional intelligence level of medical students based on multiple regression analysis
DOI:
https://doi.org/10.32782/2077-6594/2025.4/05Keywords:
emotional intelligence, Wong and Law Emotional Intelligence Scale (WLEIS), medical students, regression analysis, multivariate regression prediction modelAbstract
Emotional intelligence (EI) is a key competency for future medical professionals that influences academic performance, communication effectiveness, and stress resilience in clinical settings. Recent reviews and empirical studies demonstrate consistent associations between EI and academic performance, psychological well-being, stress-coping skills, and clinical outcomes, although the strength of these associations varies depending on context and measurement methodology. Purpose. The aim of this study was to develop a mathematical model for predicting the level of EI among 4th–6th-year students of Ternopil National Medical University using the Wong and Law Emotional Intelligence Scale (WLEIS) based on multivariate regression analysis. Materials and methods. The study was conducted using a cross-sectional design. A total of 474 students (from the 4th, 5th, and 6th years) completed the WLEIS questionnaire throughout the academic year. No analysis was performed based on the year of study. The collected data were analyzed using medical-statistical methods, including multivariate regression analysis and analysis of variance (ANOVA), to assess the impact of one or more categorical variables (factors) on a single dependent continuous variable. Additionally, a bibliosemantic method was employed to analyze the scientific literature, and a structural-logical analysis was used to build the predictive model of emotional intelligence. Results. The results indicated that the ability to recognize and understand others’ emotions was relatively high, whereas the regulation and effective use of one’s own emotions demonstrated lower scores. Conclusions. The developed mathematical model enables the prediction of EI levels among students and can be applied to design targeted educational programs and interventions aimed at enhancing emotional competence. The implementation of such strategies will contribute to improved communication, teamwork, and emotional resilience among future medical professionals. The proposed model can serve as an analytical tool for the early identification of at-risk groups concerning students’ emotional well-being and the development of targeted educational interventions.
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