Prediction of Depression through Early Maladaptive Schemas

Abstract

The aim of the present project was assessing the correlation between Young's early maladaptive schemas model and depression symptoms between non-hospitalized depressed patients and healthy group. Early maladaptive schemas (EMSs) are stable and trait-like underlying beliefs that considered as the infrastructure of recurrent and chronic psychological disorders. In this study, the correlation between these schemas and chronic depression disorders are evaluated. In this project, 47 non-hospitalized depressed patients who referred to the health care and clinical centers of Kermanshah city, during a year, were detected by using structured interview and Beck Depression Inventory (BDI-II). Also, 49 cases referred to clinics for mild problems were considered as the control group (=N96). Fifteen EMSs were measured through Young Schemas Questionnaire-Short Form Schemas (YSQ-SF). The data was analyzed using correlation and multiple regression method. The highest meaningful correlation was ascribed to the schemas of disease and vulnerability to harm (r=0.64) and the lowest one was achieved by schemas of constraints hampered (r=0.45). By using stepwise multiple regression model, with combination of four schemas of disease and vulnerability to harm, social isolation, emotional inhibition, and continence/insufficient discipline about 61% (r2=0.61) of chronic depression changes could be explained. The four schemas of EMS, vulnerability, social isolation, emotional inhibition, and continence/insufficient discipline enable to present the largest amount of variance of the dependent variable or so called chronic depression. However, schemas like failure to achieve, abandonment, mistrust, emotional deprivation, defectiveness / shame, dependency, subjugation, self-sacrifice, unrelenting standards, entitlement, and self-control cannot significantly predict the major parts of dependent variables. The findings showed that EMSs have a positive correlation with the severity of depression' and chronic depression can be predicted with a high accuracy through these schemas. The accuracy of this prediction in the rejection cut is higher than other fields.

DOI: 10.5901/mjss.2015.v6n1s1p602