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Social Network Structures of Breast Cancer Patients and the Contributing Role of Patient Navigators
Author(s) -
Gunn Christine M.,
Parker Victoria A.,
Bak Sharon M.,
Ko Naomi,
Nelson Kerrie P.,
Battaglia Tracy A.
Publication year - 2017
Publication title -
the oncologist
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.176
H-Index - 164
eISSN - 1549-490X
pISSN - 1083-7159
DOI - 10.1634/theoncologist.2016-0440
Subject(s) - medicine , social network (sociolinguistics) , social support , breast cancer , multivariate analysis , affect (linguistics) , health care , social network analysis , cancer , family medicine , social media , psychology , social psychology , world wide web , computer science , communication , economics , economic growth
Background Minority women in the U.S. continue to experience inferior breast cancer outcomes compared with white women, in part due to delays in care delivery. Emerging cancer care delivery models like patient navigation focus on social barriers, but evidence demonstrating how these models increase social capital is lacking. This pilot study describes the social networks of newly diagnosed breast cancer patients and explores the contributing role of patient navigators. Materials and Methods Twenty‐five women completed a one hour interview about their social networks related to cancer care support. Network metrics identified important structural attributes and influential individuals. Bivariate associations between network metrics, type of network, and whether the network included a navigator were measured. Secondary analyses explored associations between network structures and clinical outcomes. Results We identified three types of networks: kin‐based, role and/or affect‐based, or heterogeneous. Network metrics did not vary significantly by network type. There was a low prevalence of navigators included in the support networks (25%). Network density scores were significantly higher in those networks without a navigator. Network metrics were not predictive of clinical outcomes in multivariate models. Conclusion Patient navigators were not frequently included in support networks, but provided distinctive types of support. If navigators can identify patients with poorly integrated (less dense) social networks, or who have unmet tangible support needs, the intensity of navigation services could be tailored. Services and systems that address gaps and variations in patient social networks should be explored for their potential to reduce cancer health disparities. Implications for Practice This study used a new method to identify the breadth and strength of social support following a diagnosis of breast cancer, especially examining the role of patient navigators in providing support. While navigators were only included in one quarter of patient support networks, they did provide essential supports to some individuals. Health care providers and systems need to better understand the contributions of social supports both within and outside of health care to design and tailor interventions that seek to reduce health care disparities and improve cancer outcomes.

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