%0 Journal Article %A Mousazadeh, Omsalmeh %A Haji Alizadeh, Kobra %T Prediction of Depression Based on Dysfunctional Attitudes, Personality Traits, and Family Communication Patterns among Patients with Epilepsy %J The Neuroscience Journal of Shefaye Khatam %V 5 %N 4 %U http://shefayekhatam.ir/article-1-1423-en.html %R 10.18869/acadpub.shefa.5.4.47 %D 2017 %K Depression, Epilepsy, Patients, %X Introduction: Patients with epilepsy are markedly at the risk of depression. It is important to find out the causes of depression in these patients in order to provide comprehensive health care services. Therefore, this study was done to predict the propensity to the depression based on dysfunctional attitudes, personality traits, and family communication patterns among epileptic patients. Materials and Methods: The present research was a descriptive-correlational study. The population included all patients with epilepsy who referred to the hospitals and medical clinic of Bandar Abbas during the year 2015 from which 150 patients were selected using convenience sampling method. Beck depression Inventory, NEO Five-Factor Inventory, Dysfunctional Attitudes Scale, and Family Communication Patterns Questionnaire were used for data collection. Results: Personality traits of neuroticism, extraversion, agreeableness, and openness as well as communication pattern of conformity can explain together %59.2 of depression variance in these patients. However, conscientiousness personality trait, dysfunctional attitudes, and communication pattern of conversation cannot predict depression. Conclusion: According to the findings of this study, it can be suggested that training and intervention on improving personality traits and family communication patterns are effective methods to decreasing depression of patients with epilepsy. %> http://shefayekhatam.ir/article-1-1423-en.pdf %P 47-56 %& 47 %! %9 Research --- Open Access, CC-BY-NC %L A-10-227-3 %+ Department of Psychology, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran %G eng %@ 2322-1887 %[ 2017