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Perinatal depression and anxiety in Ghana: a qualitative study of women's perspectives on AI-driven interventions
Ayomide Oluwaseyi Oladosu1, Clinton Sekyere Frempong2, Uchechi Shirley Anaduaka3
1School of Transdisciplinary Studies, The Hang Seng University of Hong Kong, Shatin, Hong Kong.
Background:
The perinatal period is critical for maternal and child health, yet many women experience perinatal mental illnesses, particularly perinatal depression and anxiety. In Ghana, the burden of perinatal depression and anxiety is exacerbated by socioeconomic challenges and limited access to mental health services. This study explores the perceptions and experiences of pregnant women and new mothers regarding the role of artificial intelligence in addressing perinatal depression and anxiety.
Methods:
A qualitative approach utilizing focus group discussions was employed to gather insights from 15 participants, including 8 pregnant women and 7 new mothers, in Accra, Ghana. Thematic analysis was used to identify key themes related to experiences with perinatal depression and anxiety and attitudes toward artificial intelligence technologies.
Results:
Four main themes emerged: awareness of perinatal depression and anxiety and its emotional impact, scepticism and fear regarding artificial intelligence's ability to provide emotional support, perceived benefits and significant barriers associated with artificial intelligence tools. The participants acknowledged the potential for artificial intelligence to aid in self-monitoring and education but expressed concerns about privacy, trust, and the fear of losing human interaction in care.
Conclusion:
This study highlights the complex interplay of awareness, emotional experiences, and attitudes toward artificial intelligence among perinatal women in Ghana. This highlights the need for culturally sensitive educational initiatives and ethical guidelines for artificial intelligence integration in maternal health, aiming to enhance mental health outcomes for women and their families in low- and middle-income contexts.
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