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A qualitative analysis on the experiences of mothers of children in burn ICU: "She burned on the outside, me inside..."

Sabri KarahanMelike Ayça Ay KaatsizAtiye ErbasYunus Kaya
Published in: Journal of burn care & research : official publication of the American Burn Association (2023)
Mothers closely follow the complex process due to the burning of their children. Caring for and supporting the child can pose various challenges for mothers. With the phenomenological method, this study was conducted to investigate mothers' experiences staying with their children in the pediatric burn intensive care unit. Twelve mothers participated in the study. The semi-structured face-to-face interviews obtained data. After each interview, the research team transcribed the interviews verbatim. Interpretive Phenomenological Analysis was used to analyze the data. The experiences of the mothers were classified in four contexts as a result of the interpretative phenomenological analysis; "first reactions to burn trauma" related to the awareness that the child has been burned, "being a mother in the burn intensive care unit" related to caring for the child as a companion in the burn intensive care unit, "coping" related to how they cope with the problems throughout the whole process, and "requirements" regarding the subjects it needs in the process. It was determined that mothers went through a physically and emotionally challenging process from the beginning of the burn trauma and throughout the intensive care unit. During this challenging process, it was observed that mothers could not use effective coping methods and did not receive the necessary professional support. In line with these results, it is recommended that psychological support programs be applied to the mothers and that care focused on the needs of the mothers should be provided.
Keyphrases
  • intensive care unit
  • mental health
  • young adults
  • palliative care
  • depressive symptoms
  • wound healing
  • public health
  • social support
  • electronic health record
  • machine learning
  • quality improvement
  • drug induced