scholarly journals Understanding perceptions of recovery from psychological distress in Sierra Leone through qualitative comparative analysis

Author(s):  
Kanykey Jailobaeva ◽  
Rebecca Horn ◽  
Abdulai Jawo Bah ◽  
Giulia Loffreda ◽  
Alastair Ager

AbstractQualitative comparative analysis (QCA) is a relatively new method that examines causal complexity. Its use in mental health research is nascent. In low-income and fragile settings, with weak mental health service provision, identifying pathways of recovery from psychological distress can inform the appropriate deployment of scarce community and public resources. This paper examines the use of QCA to identify predictors of recovery in Sierra Leone. Our study explored lay perceptions of the signs of recovery from psychological distress caused by such events as the loss of a family member, severe sickness, and loss of a relationship. The data drew upon 75 interviews with women and men, across four districts of Sierra Leone, who described the signs of recovery from psychological distress they have observed in one person known to them. The truth table generated through QCA software indicated two signs of recovery— work/study and healthy relations—to be the most prevalent across different combinations of predictive factors. Further analysis of the truth table and sub-set relations suggested that work/study and healthy relations frequently served as sufficient conditions for reported recovery from distress to occur. QCA provided a means to identify sufficient predictive factors for recovery from psychological distress to occur. The findings suggest that to enable recovery from psychological distress, support needs to be broad and bring together services that will enable individuals to improve their social and relational wellbeing. Responses to distress need to involve a wide range of community-based stakeholders who will help individuals to engage in constructive activity and strengthen relations with their family members, friends, and the broader community. QCA is potentially well-positioned to unpack complexity in mental health research.

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