Login / Signup

War and displacement stressors and coping mechanisms of Syrian urban refugee families living in Istanbul.

Aliriza ArënliuNathan BertelsenRahaf SaadHussam AbdulazizStevan Merrill Weine
Published in: Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association (Division 43) (2019)
The overall purpose of this study was to achieve a contextual understanding of war and displacement stressors and coping mechanisms among urban refugee families from Syria living in Istanbul. This study was informed primarily by Walsh's family resilience framework and Weine's Family Consequences of Refugee Trauma empirical model. Qualitative family interviews were conducted with a purposive sample of 30 Syrian refugee families from the Çapa and Esenler neighborhoods of Istanbul. Data were analyzed using a grounded theory approach and Atlas/ti software. The analysis identified a total of 21 war and displacement stressors for families across 3 categories: (a) Surviving war and border crossing; (b) Living as urban refugees, and; (c) Parenting children in refuge. The analysis also identified a total of 16 coping mechanisms for families across 4 themes: (a) Flexible and reciprocal family organization; (b) Hopeful family beliefs and communication; (c) Staying connected with family in Syria and in exile, and; (d) Making the best of living in a new country. These findings underlie the need for several practice and policy priorities including: (a) Increasing the number of children attending Turkish schools and decreasing child labor; (b) Incorporating faith into psycho-social and mental health interventions, and; (c) Developing family focused interventions conducted by community-based lay providers that draw upon empirical models of family stressors and coping. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Keyphrases
  • mental health
  • social support
  • healthcare
  • depressive symptoms
  • young adults
  • emergency department
  • physical activity
  • climate change
  • deep learning
  • trauma patients