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Coping strategies among older adults living with HIV/AIDS with history of childhood sexual abuse.

Daniel A AmoatikaMonique J BrownPrince Nii Ossah AddoAmandeep Kaur
Published in: International journal of STD & AIDS (2023)
Background: Childhood sexual abuse (CSA) may be a risk factor for poor mental health in adulthood. Survivors may experience emotions detrimental to their social and mental wellbeing. Some of these emotions may include anger, fear, rage, helplessness, guilt, shame, which may impact their coping strategies. The aim of this study was to determine the association between CSA and coping among older adults living with HIV (OALH). Method: Data were obtained from 91 OALH via convenience sampling. The participants were recruited from an immunology clinic and were at least 50 years or older and living with HIV. CSA was operationalized using questions from the Adverse Childhood Experiences Questionnaire. Coping was assessed using the Brief COPE Inventory. Crude and adjusted linear regression models, controlling for age, sex, race, gender, and income were used to determine the association between CSA and each coping subscale. The analyses were conducted in SAS version 9.4. Results: Crude analyses showed statistically significant associations between CSA and specific coping strategies: humor (β = 1.244; p = 0.0018), religion (β = 1.122; p = 0.0291), Self-blame (β = 1.103; p = 0.0154), planning β = 1.197; p = 0.0196), venting (β = 1.218; p = 0.0063), substance use (β = 0.828; p = 0.0335) and instrumental support (β = 0.949; p = 0.0416) After adjusting for sociodemographic characteristics, there was a statistically significant association between CSA and humor (β = 1.321; p = 0.0048) and self-blame (β = 1.046; p = 0.0382). Conclusion: OALH with a history of CSA were more likely to use humor and self-blame as coping strategies. Trauma-informed interventions should be geared towards decreasing self-blame for OALH who are CSA survivors.
Keyphrases
  • depressive symptoms
  • mental health
  • social support
  • physical activity
  • healthcare
  • young adults
  • early life
  • artificial intelligence
  • machine learning