The impact of social network change due to spousal loss: A qualitative study on experiences of older adults who are aging in place.

Spousal loss due to nursing home admission or death is challenging for the well-being of the remaining partner and for aging in place. We explored: "How does social network change due to spousal loss impact older adults who are aging in place?." In-depth interviews were held with six older women who were aging in place and who lost their spouses in the past two years. Narrative analysis was conducted. Results indicate that the impact varies in three dimensions and that variations within dimensions follow three themes. The results emphasize the complexity of impact and the urgent need for a person-centred approach toward older adults after spousal loss.

Discovering trends of social interaction behavior over time: An introduction to relational event modeling : Trends of social interaction.

Real-life social interactions occur in continuous time and are driven by complex mechanisms. Each interaction is not only affected by the characteristics of individuals or the environmental context but also by the history of interactions. The relational event framework provides a flexible approach to studying the mechanisms that drive how a sequence of social interactions evolves over time. This paper presents an introduction of this new statistical framework and two of its extensions for psychological researchers. The relational event framework is illustrated with an exemplary study on social interactions between freshmen students at the start of their new studies. We show how the framework can be used to study: (a) which predictors are important drivers of social interactions between freshmen students who start interacting at zero acquaintance; (b) how the effects of predictors change over time as acquaintance increases; and (c) the dynamics between the different settings in which students interact. Findings show that patterns of interaction developed early in the freshmen student network and remained relatively stable over time. Furthermore, clusters of interacting students formed quickly, and predominantly within a specific setting for interaction. Extraversion predicted rates of social interaction, and this effect was particularly pronounced on the weekends. These results illustrate how the relational event framework and its extensions can lead to new insights on social interactions and how they are affected both by the interacting individuals and the dynamic social environment.

The impact of social network change and health decline: a qualitative study on experiences of older adults who are ageing in place.

BACKGROUND: Older adults prefer to age in place. Social network change and health decline challenge ageing in place, as stressors that make age-related advantages disappear. The aim of this study was to explore social network change and health decline and its impact on older adults who are ageing in place. METHOD: In-depth interviews (n = 16) were conducted with older adults who were ageing in place and who were experiencing health decline and social network change. Procedures for grounded theory building were followed to analyse the interviews with respondents who were discharged from the hospital less than 4 months ago (n = 7). Narrative analysis was conducted to reach a deeper understanding of the expected complexity of experiences of this targeted sample. RESULTS: Results encompass a typology with four types of impact: A. Sneak preview of old age, B. Disruptive transition into old age, C. Drastically ageing, and D. Steadily ageing. Additionally, indications were found that older adults should be able to move along the four types of impact and ideally could end up in quartile D, experiencing little or no impact at all (anymore). CONCLUSION: The results present an optimistic view on the possibilities of older adults to continue ageing in place despite experiencing unavoidable and uncontrollable stressors in life. Also, the results provide leads for practice, to develop an action perspective for home care nurses and gerontological social workers to determine and reduce the impact of social network change and health decline on older adults who are ageing in place. Suggestions for further research would be to unravel how to detect temporal setbacks in successful ageing in place.

A review of applications of the Bayes factor in psychological research.

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The article is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Collective intelligence in teams: Contextualizing collective intelligent behavior over time.

Collective intelligence (CI) in organizational teams has been predominantly understood and explained in terms of the quality of the outcomes that the team produces. This manuscript aims to extend the understanding of CI in teams, by disentangling the core of actual collective intelligent team behavior that unfolds over time during a collaboration period. We posit that outcomes do support the presence of CI, but that collective intelligence itself resides in the interaction processes within the team. Teams behave collectively intelligent when the collective behaviors during the collaboration period are in line with the requirements of the (cognitive) tasks the team is assigned to and the (changing) environment. This perspective results in a challenging, but promising research agenda armed with new research questions that call for unraveling longitudinal fine-grained interactional processes over time. We conclude with exploring methodological considerations that assist researchers to align concept and methodology. In sum, this manuscript proposes a more direct, thorough, and nuanced understanding of collective intelligence in teams, by disentangling micro-level team behaviors over the course of a collaboration period. With this in mind, the field of CI will get a more fine-grained understanding of what really happens at what point in time: when teams behave more or less intelligently. Additionally, when we understand collectively intelligent processes in teams, we can organize targeted interventions to improve or maintain collective intelligence in teams.

Dynamic relational event modeling: Testing, exploring, and applying.

The relational event model (REM) facilitates the study of network evolution in relational event history data, i.e., time-ordered sequences of social interactions. In real-life social networks it is likely that network effects, i.e., the parameters that quantify the relative importance of drivers of these social interaction sequences, change over time. In these networks, the basic REM is not appropriate to understand what drives network evolution. This research extends the REM framework with approaches for testing and exploring time-varying network effects. First, we develop a Bayesian approach to test whether network effects change during the study period. We conduct a simulation study that illustrates that the Bayesian test accurately quantifies the evidence between a basic ('static') REM or a dynamic REM. Secondly, in the case of the latter, time-varying network effects can be studied by means of a moving window that slides over the relational event history. A simulation study was conducted that illustrates that the accuracy and precision of the estimates depend on the window width: narrower windows result in greater accuracy at the cost of lower precision. Third, we develop a Bayesian approach for determining window widths using the empirical network data and conduct a simulation study that illustrates that estimation with empirically determined window widths achieves both good accuracy for time intervals with important changes and good precision for time intervals with hardly any changes in the effects. Finally, in an empirical application, we illustrate how the approaches in this research can be used to test for and explore time-varying network effects of face-to-face contacts at the workplace.

Exploring the impact of social network change: Experiences of older adults ageing in place.

Social networks are sources of support and contribute to the well-being of older adults who are ageing in place. As social networks change, especially when accompanied by health decline, older adults' sources of support change and their well-being is challenged. Previous studies predominantly used quantitative measures to examine how older adults' social networks change. Alternatively, this study explores the impact of changing social networks on older adults' lives by examining their personal experiences. We held four focus groups, two with a total of 14 older adults who are ageing in place and receiving home care and two with a total of 20 home-care nurses from different regions and organisations in the Netherlands. Subsequently, an expert team of home-care professionals and managers discussed and verified the results. Procedures for grounded theory building were used for analysis. We revealed four themes of high-impact experiences: (a) struggling with illness/death of the spouse; (b) working out a changing relationship with (grand)children; (c) regretting the loss of people they have known for so long and (d) feeling dependent and stressed when helpers enter the network. Also, network dynamics were found to follow three consecutive stages: (a) awareness of social network change; (b) surprise when social network change actually occurs and (c) acceptance and adjusting to new circumstances. Together, the four themes of experiences and three stages of network change form an integrative model of the role of social network dynamics for older adults' lives when ageing in place.

[When do bad apples not spoil the barrel? Negative relationships in teams, team performance, and buffering mechanisms].

The study of negative relationships in teams has primarily focused on the impact of negative relationships on individual team member attitudes and performance in teams. The mechanisms and contingencies that can buffer against the damaging effects of negative relationships on team performance have received limited attention. Building on social interdependence theory and the multilevel model of team motivation, we examine in a sample of 73 work teams the team-level attributes that foster the promotive social interaction that can neutralize the adverse effect of negative relationships on team cohesion and, consequently, on team performance. The results indicate that high levels of team-member exchange as well as high task-interdependence attenuate how team cohesion and team performance suffer from negative relationships. Implications for research and practice are discussed.