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Enhancing a peer supporter intervention for young mothers living with HIV in Malawi, Tanzania, Uganda, and Zambia: Adaptation and co-development of a psychosocial component
Young mothers living with HIV (YMHIV) experience heightened risks to their mental health, as their transition to adulthood is marked by social stigma, health and socioeconomic challenges. Targeted psychosocial interventions may improve the mental health of YMHIV; however, no evidence-based intervent...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9705607/ https://www.ncbi.nlm.nih.gov/pubmed/35634944 http://dx.doi.org/10.1080/17441692.2022.2081711 |
Sumario: | Young mothers living with HIV (YMHIV) experience heightened risks to their mental health, as their transition to adulthood is marked by social stigma, health and socioeconomic challenges. Targeted psychosocial interventions may improve the mental health of YMHIV; however, no evidence-based interventions have been developed for this group. Peer support models, more common for youth living with HIV, show promise as a design to reach YMHIV in a non-stigmatising way. This manuscript describes the process of adapting and co-developing an evidence-based psychosocial component (Boost) of a larger intervention called Ask-Boost-Connect-Discuss. Peer supporters in Malawi, Tanzania, Uganda, and Zambia used ABCD to guide group sessions with YMHIV. The research team partnered with an implementing partner, Paediatric-Adolescent Treatment Africa, to undertake this work in three phases: 1) formative research, 2) content adaptation and development, and 3) consultation, refinement, and modification. YMHIV (n = 4), peer supporters (n = 21), and technical advisors (n = 4) were engaged as co-developers, shaping the resulting Boost intervention component at each phase. Peer support models may effectively reach young mothers, and consultation, co-creation, and integration with existing programming can offer rich insights to inform these models. We discuss the implications and promise of this approach. |
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