Increased genetic testing in personalized medicine presents unique challenges for couples, including managing disease risk and potential discrimination as a couple. This study investigated couples’ conflicts and support gaps as they coped with perceived genetic discrimination. We also explored the degree to which communal coping was beneficial in reducing support gaps and ultimately stress. Dyadic analysis of married adults (N = 266, 133 couples), in which one person had the genetic risk for serious illness, showed that perceived discrimination predicted more frequent conflicts about alpha-1 antitrypsin deficiency-related treatment, privacy boundaries, and finances, which, in turn, predicted wider gaps in emotion and esteem support, and greater stress for both spouses. Communal coping predicted lower support gaps for both partners and marginally lower stress.

Although scholars have argued that people actively shape and reshape their social networks (e.g., Parks, 2016), this aspect of relational development has received little attention. This study sought to determine if people’s self-perceptions of interpersonal communication skills translated into behavior that led to relationship formation in a new network. A 9-month longitudinal social network analysis (N = 94) of the residents of a first-year university residence hall using Facebook tie data was conducted to assess network changes. Results indicate that both self-perceived network centrality in a hypothetical friendship sociogram (Smith & Fink, 2015) and self-reported connector scores (Boster et al., 2011) are good longitudinal predictors of relationship development. Those who began by self-identifying as central, became central.

The Arctic environment is experiencing profound and rapid changes that will have far-reaching implications for resilient and sustainable development at the local and global levels. To achieve sustainable Arctic futures, it is critical to equip policymakers and global and regional stake- and rights-holders with knowledge and data regarding the ongoing changes in the Arctic environment. Community monitoring is an important source of environmental data in the Arctic but this research argues that community-generated data are under-utilized in the literature. A key challenge to leveraging community-based Arctic environmental monitoring is that it often takes the form of large, unstructured data consisting of field documents, media reports, and transcripts of oral histories. In this study, we integrated two computational approaches—topic modeling and network analysis—to identify environmental changes and their implications for resilience and sustainability in the Arctic. Using data from community monitoring reports of unusual environmental events in the Arctic that span a decade, we identified clusters of environmental challenges: permafrost thawing, infrastructure degradation, animal populations, and fluctuations in energy supply, among others. Leveraging visualization and analytical techniques from network science, we further identified the evolution of environmental challenges over time and contributing factors to the interconnections between these challenges. The study concludes by discussing practical and methodological contributions to Arctic resiliency and sustainability.