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.

Shared understanding and task-interdependence in nursing interns' collaborative relations: A social network study of vocational health care internships in the Netherlands

Shared understanding among collaborators is a key element of delivering successful interprofessional care and a main challenge for professional education concerns nurturing such understanding among students. We assessed how nursing students perceived different levels of shared understanding in their collaborations with others in clinical internships. We analyse the collaborative networks of interns to examine whether individual factors (attitudes, perceptions of collaborative cultures, and motivation) or relational factors among collaborators (task-interdependence, cooperation frequency, and interprofessional and hierarchical roles) affect shared understanding among 150 Dutch nursing interns and their collaborators (n = 865). Theoretically, we stress the importance of focusing on collaborative relations in interprofessional care settings. Multilevel models distinguish two levels in explaining the variation in shared understanding, nesting collaborative relationships within individuals. Results indicate merely 37.4% of found variation of shared understanding could be attributed to individual-level factors (variation between interns), while 62.6% of variation is found within interns, showing that shared understanding differs substantially between the collaborations one intern engages in. Multilevel models reveal that task-interdependence strongly predicts shared understanding in inter- and intraprofessional collaborations. We conclude that focusing on collaborative relations is essential to foster shared understanding in vocational internship programmes, and that health care organisations should pay explicit attention to task-interdependence in interns' collaborations.

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.