Design of a regression model to predict the presence of depression during pregnancy based on emotional intelligence, parental care, anxiety and stress

Abstract

Background: Emotion regulation involves the modulation of emotional experiences to facilitate goal attainment. Conversely, emotional difficulties are a pattern of emotional experiences and expressions that interfere with goal-directed behavior. Objectives: Design a new model to predict the presence of depression in women during pregnancy. Methods: Non-experimental, cross-sectional, explanatory study of depression in women during pregnancy (logistic regression) considering the variables emotional intelligence, parental care, anxiety and stress. The sample consisted of 273 pregnant women-mothers between 14 and 38 weeks pregnant, aged between 18 and 38 years, for a mean of 25.67 years (SD= 5.8). Results: The regression model is valid and significant in predicting the probability of occurrence of depression, explaining 82.4% of the variance of DV (Presence of depression) by the variables age, clarity and repair of depression dimensions. emotional intelligence, the maternal and paternal overprotection dimensions, and paternal care of the parental style variables; stress, work and single marital status. There is a 95.2% probability of success in the depression result when each of the model variables is incorporated. Conclusions: The best predictors of depression in pregnancy would be, on the one hand, higher levels or values of the variables and indicators age, reparation, maternal overprotection, paternal care, and stress, and on the other hand, low scores in the dimensions and values of clarity variables, and paternal overprotection; added to whether the woman works and is single. This combination of variables would be the individual and contextual conditions that influence said appearance.