Background: One in four families has at least one member with a mental disorder and families are the main caregivers in most patients in low and middle-income countries. Caregivers experience disruption in their routine lives, increased responsibilities, increased need for support, and changes in their network. The role of personal support networks in the health and burden of the caregivers is complex and depends on the context, cultural and socioeconomical variables. In this study, we aimed to investigate the personal support network of caregivers of patients with severe mental illness in Iran. Methods: By using a mixed-methods design, we focused on the structure and composition of caregiver networks, as well as self-perceived caregiver support. Results: We found that the support network of caregivers was mostly composed of immediate family members whom themselves were selectively chosen as a result of the multidimensional process of interaction between stigma, availability, and the perceived needs of caregivers. The participants mentioned economic and instrumental supports more frequently than emotional support, probably reflecting their unmet basic needs. Advocacy for providing formal systemic supports to caregivers, as well as interventions that expand caregivers personal support network is recommended. Conclusions: Most participants of the study were relatively isolated and had a small network of support, mostly composed of immediate family members. Stigma was a serious source of family distress for caregivers and a limiting factor in social relationships.

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Thematic analysis of personal networks involves identifying regularities in network structure and content, and grouping networks into types/clusters, to allow for a holistic understanding of social complexities. We propose an inductive approach to network thematic analysis, applying the learnings from qualitative coding, fused mixed-methods analysis, and typology development. It involves framing (changing focus by magnifying, aggregating, and graphical configuration), pattern detection (identification of underlying dimensions, sorting, and clustering), labeling, and triangulating (confirmation and fine-tuning using quantitative and qualitative approaches); applied repeatedly and emergently. We describe this approach utilized in two cases of studying support networks of caregivers.