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Surviving Child Abuse in People With Mental Illness: A Grounded Theory Approach.

Jeongmin HaYoonjung Kim
Published in: Journal of interpersonal violence (2022)
The aim of this qualitative study was to examine the process of surviving child abuse in people with a mental illness in order to develop an explanatory theory. The study utilized the grounded theory approach. Seventeen community-dwelling adults with a mental illness who had experienced child abuse were interviewed. For the in-depth interview, the lifeline interview method was used. Data were collected from July 14, 2019, to February 28, 2020. The constant comparative method was used for analysis, to identify similarities and differences between different statements, and similar phenomena or theories were compared and analyzed continuously. The central phenomena were "losing oneself" and "in a precarious state." Participants used "expressing," "standing on one's own feet," and "avoiding" as coping strategies. Observed outcomes were "making life work for them" and "living with others." The core category was "losing myself, embracing myself as someone in a precarious state, and being reborn as the master of my life." Positive religious coping, having a supportive network, and emotional or physical distance from difficult situations played a major role in surviving participants' experiences of child abuse and being victimized because of their mental illness. Our findings provide a theoretical basis for understanding people with mental illness who have survived child abuse, and suggest that opportunities for sharing their stories, facilitating self-reliance, and avoiding the causes of their difficulties all play a role in their healing process. Based on this study, it is expected that clinical experts and policy developers will be able to formulate evidence-based interventions and policies.
Keyphrases
  • mental illness
  • mental health
  • intimate partner violence
  • community dwelling
  • public health
  • healthcare
  • physical activity
  • machine learning
  • social media
  • big data