Diversity, integration, and variability of intergenerational relationships in old age: New insights from personal network research.

Relationships between family members from different generations have long been described as a source of solidarity and support in aging populations and, more recently, as a potential risk factor for COVID-19 contagion. Personal or egocentric network research offers a powerful kit of conceptual and methodological tools to study these relationships, but this has not yet been employed to its full potential in the literature. We investigate the heterogeneity, social integration, and individual correlates of intergenerational relationships in old age analyzing highly granular data on the personal networks of 230 older adults (2747 social ties) from a local survey in one of the areas of the world at the forefront of global aging trends (northern Italy). Using information on different layers in broad egocentric networks and on the structure of connectivity among the social contacts of aging people, we propose multiple conceptualizations and measures of intergenerational connectedness. Results show that intergenerational relationships are strongly integrated, but also highly diverse and variable, in older adults' social networks. Different types of intergenerational ties exist in different network layers, with various relational roles, degrees of tie strength, and patterns of association with individual and tie characteristics. We discuss how new and existing personal network data can be leveraged to consider novel questions and hypotheses about intergenerational relationships in contemporary aging families.

Interorganizational collaboration in a trauma-informed community: A network analysis of cohesion and change.

Strengthening interorganizational collaboration is critical to mitigate the impact of adverse childhood experiences (ACEs) and improve community health. We examined change in interorganizational collaboration around ACEs within Peace4Tarpon's network and investigated factors influencing collaboration. We conducted a community-wide social network analysis among 32 trauma-informed organizations in 2016 and 2018, using network analysis methods to examine interorganizational cohesion (density, transitivity, triad census) over time, and multiple regression quadratic assignment procedure to investigate factors influencing collaboration. Network cohesion measures indicated small increases in collaboration level and greater network cohesion over time. Conducting ACEs screenings was a significant factor (b = 0.237; p < 0.01) predicting likelihood of interorganizational collaboration in 2016. No assessed ACEs practices predicted collaboration in 2018, suggesting variables assessed predicted a small proportion of variance in collaboration change. Results provide a foundation for understanding how ACEs/trauma-informed practices influence collaboration and highlight implications of interorganizational collaboration. Peace4Tarpon's 2-year progress provides insights for other trauma-informed communities.

Discovering new pathways toward integration between health and sustainable development goals with natural language processing and network science.

BACKGROUND: Research on health and sustainable development is growing at a pace such that conventional literature review methods appear increasingly unable to synthesize all relevant evidence. This paper employs a novel combination of natural language processing (NLP) and network science techniques to address this problem and to answer two questions: (1) how is health thematically interconnected with the Sustainable Development Goals (SDGs) in global science? (2) What specific themes have emerged in research at the intersection between SDG 3 ("Good health and well-being") and other sustainability goals? METHODS: After a descriptive analysis of the integration between SDGs in twenty years of global science (2001-2020) as indexed by dimensions.ai, we analyze abstracts of articles that are simultaneously relevant to SDG 3 and at least one other SDG (N = 27,928). We use the top2vec algorithm to discover topics in this corpus and measure semantic closeness between these topics. We then use network science methods to describe the network of substantive relationships between the topics and identify 'zipper themes', actionable domains of research and policy to co-advance health and other sustainability goals simultaneously. RESULTS: We observe a clear increase in scientific research integrating SDG 3 and other SDGs since 2001, both in absolute and relative terms, especially on topics relevant to interconnections between health and SDGs 2 ("Zero hunger"), 4 ("Quality education"), and 11 ("Sustainable cities and communities"). We distill a network of 197 topics from literature on health and sustainable development, with 19 distinct network communities - areas of growing integration with potential to further bridge health and sustainability science and policy. Literature focused explicitly on the SDGs is highly central in this network, while topical overlaps between SDG 3 and the environmental SDGs (12-15) are under-developed. CONCLUSION: Our analysis demonstrates the feasibility and promise of NLP and network science for synthesizing large amounts of health-related scientific literature and for suggesting novel research and policy domains to co-advance multiple SDGs. Many of the 'zipper themes' identified by our method resonate with the One Health perspective that human, animal, and plant health are closely interdependent. This and similar perspectives will help meet the challenge of 'rewiring' sustainability research to co-advance goals in health and sustainability.

The structure of social support: a multilevel analysis of the personal networks of people with severe mental disorders.

BACKGROUND: For psychiatric service users suffering from severe mental disorders, the social support provided by personal social networks is essential for living a meaningful life within the community. However, the importance of the support received depend on the relations between the providers of social support. Yet this hasn't been addressed in the literature so far for people with severe mental disorders. This article seeks to investigate how characteristics of service users with severe mental disorders, their social contacts, and the pattern of relationships between those contacts influence the distribution and provision of social support to people with severe mental disorders. METHODS: We collected personal network data relating to 380 psychiatric service users from a random sample of health care providers in Belgium. We computed various measures of the structure of those networks and of the position of support persons within those networks. We conducted a multilevel analysis of the importance of the support provided by each support persons. RESULTS: The results show that the more central a support person was in the network of a service user, the more important his or her support was considered to be by the service user. Also, the denser the network in which a support person was embedded, the less important was the support he or she offers, but only for hospitalised service users. CONCLUSION: These finding highlight the collective dimension of social support. We discuss the implications for the organisation of mental health care.

Resilience and fragmentation in healthcare coalitions: The link between resource contributions and centrality in health-related interorganizational networks.

Interorganizational coalitions or collaboratives in healthcare are essential to address the health challenges of local communities, particularly during crises such as the Covid-19 pandemic. However, few studies use large-scale data to systematically assess the network structure of these collaboratives and understand their potential to be resilient or fragment in the face of structural changes. This paper analyzes data collected in 2009-2017 about 817 organizations (nodes) in 42 healthcare collaboratives (networks) throughout Florida, the third-largest U.S. state by population, including information about interorganizational ties and organizations' resource contributions to their coalitions. Social network methods are used to characterize the resilience of these collaboratives, including identification of key players through various centrality metrics, analyses of fragmentation centrality and core/periphery structure, and Exponential Random Graph Models to examine how resource contributions facilitate interorganizational ties. Results show that the most significant resource contributions are made by key players identified through fragmentation centrality and by members of the network core. Departure or removal of these organizations would both strongly disrupt network structure and sever essential resource contributions, undermining the overall resilience of a collaborative. Furthermore, one-third of collaboratives are highly susceptible to disruption if any fragmentation-central organization is removed. More fragmented networks are also associated with poorer health-system outcomes in domains such as education, health policy, and services. ERGMs reveal that two types of resource contributions - community connections and in-kind resource sharing - are especially important to facilitate the formation of interorganizational ties in these coalitions.

Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals.

The United Nations' (UN) Sustainable Development Goals (SDGs) are heterogeneous and interdependent, comprising 169 targets and 231 indicators of sustainable development in such diverse areas as health, the environment, and human rights. Existing efforts to map relationships among SDGs are either theoretical investigations of sustainability concepts, or empirical analyses of development indicators and policy simulations. We present an alternative approach, which describes and quantifies the complex network of SDG interdependencies by applying computational methods to policy and scientific documents. Methods of Natural Language Processing are used to measure overlaps in international policy discourse around SDGs, as represented by the corpus of all existing UN progress reports about each goal (N = 85 reports). We then examine if SDG interdependencies emerging from UN discourse are reflected in patterns of integration and collaboration in SDG-related science, by analyzing data on all scientific articles addressing relevant SDGs in the past two decades (N = 779,901 articles). Results identify a strong discursive divide between environmental goals and all other SDGs, and unexpected interdependencies between SDGs in different areas. While UN discourse partially aligns with integration patterns in SDG-related science, important differences are also observed between priorities emerging in UN and global scientific discourse. We discuss implications and insights for scientific research and policy on sustainable development after COVID-19.

The isolation paradox: A comparative study of social support and health across migrant generations in the U.S.

Social isolation and international migration have potentially adverse effects on physical and mental health, and may compound each other when migrants have limited access to supportive social networks. This problem may be particularly serious in older age groups, who are more vulnerable to illness and isolation. We analyze population representative data from a detailed survey of social networks and health in the San Francisco Bay Area, U.S., to compare access to different types of social support and health outcomes among first-generation migrants, second-generation migrants, and nonmigrants between 50 and 70 years old (N = 674). We find that first-generation migrants report systematically lower levels of social support and poorer self-rated health compared to nonmigrants, even after controlling for sociodemographic characteristics. While social support is strongly and positively associated with health in the general population, this relationship is null or, in some cases, reversed among migrants in the first and second generations. These results provide further evidence that migration operates as an adverse social determinant of health, and suggest an isolation paradox: migrants are healthier than nonmigrants only at very low levels of social support, and they do not experience the same beneficial health effects of social support as nonmigrants.

Organizational partnerships for a trauma-informed community: A community-wide social network study.

We investigated whether Peace4Tarpon's trauma-responsive community capacity activities led to greater collaboration among community partners. We conducted longitudinal social network analysis (SNA) among organizations within Peace4Tarpon's network in 2016 and 2018 to capture cooperation around adverse childhood experiences-related topics. We examined network structure, cohesion, organizational collaboration, and associations between centrality and organizational practices. Peace4Tarpon's network included diverse sectors, with a group of organizations forming the network core and collaborating over time. The network displayed a small increase in cohesion, more cross-sector collaboration, and less heterophily over time. We found a significant difference between the mean betweenness centralities of organizations who assessed resilience and those who did not in the 2018 average union network. This is one of the first studies using SNA to investigate a trauma-informed community network. Findings from this type of analysis may assist community organizations in strengthening outreach and strategically engaging organizations within a trauma-informed network.

Great minds think alike, or do they often differ? Research topic overlap and the formation of scientific teams.

Over the last century scientific research has become an increasingly collaborative endeavor. Commentators have pointed to different factors which contribute to this trend, including the specialization of science and growing need for diversity of interest and expertise areas in a scientific team. Very few studies, however, have precisely evaluated how the diversity of interest topics between researchers is related to the emergence of collaboration. Existing theoretical arguments suggest a curvilinear relationship between topic similarity and collaboration: too little similarity can complicate communication and agreement, yet too much overlap can increase competition and limit the potential for synergy. We test this idea using data on six years of publications across all disciplines at a large U.S. research university (approximately 14,300 articles, 12,500 collaborations, and 3,400 authors). Employing topic modelling and network statistical models, we analyze the relationship between topic overlap and the likelihood of coauthorship between two researchers while controlling for potential confounders. We find an inverted-U relationship in which the probability of collaboration initially increases with topic similarity, then rapidly declines after peaking at a similarity "sweet spot". Collaboration is most likely at low-to-moderate levels of topic overlap, which are substantially lower than the average self-similarity of scientists or research groups. These findings - which we replicate for different units of analysis (individuals and groups), genders of collaborators, disciplines, and collaboration types (intra- and interdisciplinary) - support the notion that researchers seek collaborators to augment their scientific and technical human capital. We discuss implications for theories of scientific collaboration and research policy.

Structure in personal networks: Constructing and comparing typologies.

A recurrent finding in personal network research is that individual and social outcomes are influenced not just by the kind of people one knows, but also by how those people are connected to each other: that is, by the structure of one's personal network. The different ways in which a person's social contacts know and interact with each other reflect broader variations in personal communities and social structures, and shape patterns and processes of social capital, support, and isolation. This article proposes a method to identify typologies of network structure in large collections of personal networks. The method is illustrated with an application to six datasets collected in widely different circumstances and using various survey instruments. Results are compared with those from another recently introduced method to extract structural typologies of egocentric networks. Findings show that personal network structure can be effectively summarized using just three measures describing results of the Girvan-Newman algorithm for cohesive subgroup detection. Structural typologies can then be extracted through cluster analysis on the three variables, using well-known clustering quality statistics to select the optimal typology. Both typology detection methods considered in the article capture significant variation in personal network structures, but substantial levels of disagreement and cross-classification emerge between them. I discuss differences and similarities between the methods, and potential applications of the proposed typologies to substantive research on a variety of topics, including structures and transformations of personal communities, social support, and social capital.

Who and Where: A Socio-Spatial Integrated Approach for Community-Based Health Research.

Social and spatial characteristics of a population often interact to influence health outcomes, suggesting a need to jointly analyze both to offer useful insights in community health. However, researchers have used either social or spatial analyses to examine community-based health issues and inform intervention programs. We propose a combined socio-spatial analytic approach to develop a social network with spatial weights and a spatial statistic with social weights, and apply them to an ongoing study of mental and physical well-being of rural Latino immigrants in North Florida, USA. We demonstrate how this approach can be used to calculate measures, such as social network centrality, support contact dyads, and spatial kernel density based on a health survey data. Findings reveal that the integrated approach accurately reflected interactions between social and spatial elements, and identified community members (who) and locations (where) that should be prioritized for community-based health interventions.

Detecting and analyzing research communities in longitudinal scientific networks.

A growing body of evidence shows that collaborative teams and communities tend to produce the highest-impact scientific work. This paper proposes a new method to (1) Identify collaborative communities in longitudinal scientific networks, and (2) Evaluate the impact of specific research institutes, services or policies on the interdisciplinary collaboration between these communities. First, we apply community-detection algorithms to cross-sectional scientific collaboration networks and analyze different types of co-membership in the resulting subgroups over time. This analysis summarizes large amounts of longitudinal network data to extract sets of research communities whose members have consistently collaborated or shared collaborators over time. Second, we construct networks of cross-community interactions and estimate Exponential Random Graph Models to predict the formation of interdisciplinary collaborations between different communities. The method is applied to longitudinal data on publication and grant collaborations at the University of Florida. Results show that similar institutional affiliation, spatial proximity, transitivity effects, and use of the same research services predict higher degree of interdisciplinary collaboration between research communities. Our application also illustrates how the identification of research communities in longitudinal data and the analysis of cross-community network formation can be used to measure the growth of interdisciplinary team science at a research university, and to evaluate its association with research policies, services or institutes.

The emergence of team science: Understanding the state of adoption research through social network analysis.

The notion of team science has recently gained popularity in European and American health sciences considering increasing evidence that scientific collaboration produces higher-impact research and that complex scientific problems are better investigated by interdisciplinary teams. While publication metrics indicate adoption research is expanding, the comprehensive structure of adoption studies as a scientific field has not been formally evaluated for collaborative and cross-disciplinary activity. This article aims to elucidate the structure, composition, and dynamics of scientific relationships within adoption research that may inform research and practice strategies, competencies, and cohesion within the field. Using social network analysis, we extracted bibliographic data on 2767 peer-reviewed adoption-related articles from 1930s to 2014 and evaluated the resulting co-authorship and co-citation networks. We found that adoption research has grown substantially over the last 25 years, and is conducted in varied disciplines, with increasing collaboration across geography and disciplinary areas. The co-authorship and co-citation networks are approaching numeric thresholds and structural configurations distinctive of well-established and more institutionalized fields of study. These findings reveal the maturation of adoption studies as a team science and argue for the development of institutional mechanisms that support such evolution. Implications for professional and research planning are discussed.

FBO Leaders' Perceptions of the Psycho-social Contexts for Rural Latinos.

Latinos comprise the largest minority rural population in the US, and they are often exposed to adverse social health determinants that can detrimentally affect their mental health. Guided by community-based participatory research (CBPR) principles, this study aimed to describe faith-based organizations (FBOs) leaders' perceptions of the contexts affecting the mental well-being of rural Latino immigrants and potential approaches to mental health promotion for these immigrants. This is a descriptive, qualitative arm of a larger study in which community-academic members have partnered to develop a culturally-tailored mental health promotion intervention among rural Latinos. FBO leaders (N = 15) from different denominations in North Florida were interviewed until saturation was reached. FBO leaders remarked that in addition to religiosity, which Latinos already have, more community building and involvement are necessary for the promotion of mental health.

Designing a CTSA-Based Social Network Intervention to Foster Cross-Disciplinary Team Science.

This paper explores the application of network intervention strategies to the problem of assembling cross-disciplinary scientific teams in academic institutions. In a project supported by the University of Florida (UF) Clinical and Translational Science Institute, we used VIVO, a semantic-web research networking system, to extract the social network of scientific collaborations on publications and awarded grants across all UF colleges and departments. Drawing on the notion of network interventions, we designed an alteration program to add specific edges to the collaboration network, that is, to create specific collaborations between previously unconnected investigators. The missing collaborative links were identified by a number of network criteria to enhance desirable structural properties of individual positions or the network as a whole. We subsequently implemented an online survey (N = 103) that introduced the potential collaborators to each other through their VIVO profiles, and investigated their attitudes toward starting a project together. We discuss the design of the intervention program, the network criteria adopted, and preliminary survey results. The results provide insight into the feasibility of intervention programs on scientific collaboration networks, as well as suggestions on the implementation of such programs to assemble cross-disciplinary scientific teams in CTSA institutions.