https://doi.org/10.61152/HDNZ4028
Jasmine Fernandez1, Michaela Bonnett1, Teri Garstka2 and Meaghan Kennedy1
1Orange Sparkle Ball, 2Social Innovation Labs, The University of Kansas
Series: Sunbelt 2024
Original Publication Date: June 25th, 2024
Publisher: Orange Sparkle Ball
Abstract
Exploring Social Care Network Structures
This research is grounded in the theory that scale-free networks form between many organizations in a community when coordinating social care services and influential hubs in the network emerge (Barabási & Réka, 1999).We explore the variability in the structures of social care networks, focusing on how the diverse needs of community members and the array of providers influence these structures. We posit that the architecture of these networks may hold the key to discerning patterns in community health and social outcomes.
Our study examines the resilience of social care networks, defining them as systems designed to enhance interactions among all nodes to meet diverse community needs. We discuss community as a network and community resilience as a process, introducing three key properties—scale-free, small world, and hubness/information spreading scores, for understanding network resilience.
We analyzed 20 social care networks, which have been active over an 18-month period using the referral technology tool to send and receive service referrals, providing raw interaction data among organizational nodes. We focused on two primary objectives: 1) Social care networks are more likely to exhibit scale-free properties and contain influential hubs; and 2) There is significant variability among social care networks in terms of scale-free properties and centrality measures.
Using the three properties—small world, scale-free, and hubness/information spreading scores—we classified the 20 social care networks into different structural profiles. We analyzed node,edge radius, diameter, to understand the network structure characteristics. Our findings highlighted four distinct network structures, which we ranked from most to least resilient. We discussed the implications of these structures on community-level outcomes, including the potential centralized vulnerability when hubs and information spreaders overlap, creating efficiency during normal operations but also increasing vulnerability to disruptions.
Our findings offer insights into the emergent properties of complex systems, particularly in networks intentionally designed to enhance resilience and meet diverse community needs. We conclude by discussing the variability in centrality and structural metrics within the identified groups and propose future research directions to explore the long-term impact of these network structures.
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