Exploring the stability of communication network metrics in a dynamic nursing context

Network stability is of increasing interest to researchers as they try to understand the dynamic processes by which social networks form and evolve. Because hospital patient care units (PCUs) need flexibility to adapt to environmental changes (Vardaman et al., 2012), their networks are unlikely to be uniformly stable and will evolve over time. This study aimed to identify a metric (or set of metrics) sufficiently stable to apply to PCU staff information sharing and advice seeking communication networks over time. Using Coefficient of Variation, we assessed both Across Time Stability (ATS) and Global Stability over four data collection times (Baseline and 1, 4, and 7 months later). When metrics were stable using both methods, we considered them "super stable." Nine metrics met that criterion (Node Set Size, Average Distance, Clustering Coefficient, Density, Weighted Density, Diffusion, Total Degree Centrality, Betweenness Centrality, and Eigenvector Centrality). Unstable metrics included Hierarchy, Fragmentation, Isolate Count, and Clique Count. We also examined the effect of staff members' confidence in the information obtained from other staff members. When confidence was high, the "super stable" metrics remained "super stable," but when low, none of the "super stable" metrics persisted as "super stable." Our results suggest that nursing units represent what Barker (1968) termed dynamic behavior settings in which, as is typical, multiple nursing staff must constantly adjust to various circumstances, primarily through communication (e.g., discussing patient care or requesting advice on providing patient care), to preserve the functional integrity (i.e., ability to meet patient care goals) of the units, thus producing the observed stability over time of nine network metrics. The observed metric stability provides support for using network analysis to study communication patterns in dynamic behavior settings such as PCUs.National Institute of General Medical Sciences of the National Institutes of HealthOpen access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Toward an Interoperable Dynamic Network Analysis Toolkit

To facilitate the analysis of real and simulated data on groups, organizations and societies, tools and measures are needed that can handle relational or network data that is multi-mode, multi-link and multi-time period in which nodes and edges have attributes with possible data errors and missing data. The integrated CASOS dynamic network analysis toolkit described in this paper is an interoperable set of scalable software tools. These tools form a toolchain that facilitate the dynamic extraction, analysis, visualization and reasoning about key actors, hidden groups, vulnerabilities and changes in such data at varying levels of fidelity. We present these tools and illustrate their capabilities using data collected from a series of 368 texts on an organizational system interfaced with covert networks in the Middle East

DyNetML: A Robust Interchange Language for Rich Social Network Data 1 Abstract

We define a universal data interchange format to enable exchange of rich social network data and improve compatibility of analysis and visualization tools. DyNetML is an XML-derived language that provides means to express rich social network data. DyNetML also provides an extensible facility for linking anthropological, process description and other data with social networks. DyNetML has been implemented and in use by the CASOS group at Carnegie Mellon University as a data interchange format. We have also implemented parsing and conversion software for interoperability with other software packages

An Integrated Approach to the Collection and Analysis of Network Data

To facilitate the analysis of real and simulated data on groups, organizations and societies tools and measures are needed that can handle relational or network data that is multi-mode, multi-link, and multi-time period, in which both nodes and edges have attributes and there are possible errors in the data. The integrated CMU dynamic network analysis toolset described in this paper enables the coding, analysis, and visualization of such data. Herein we present these tools and illustrate there interoperability and capabilities using data collected from a series of 247 texts on a group in the Mideast. Contact

Destabilizing Terrorist Networks

Most people have at least an intuitive understanding of hierarchies, how they work, and how to affect their behavior. However, covert organizations, such as terrorist organizations, have network structures that are distinct from those in typical hierarchical organizations. Their structure is distinct from the organizations that most people in western culture are used to dealing with. In particular, they tend to be more cellular and distributed. As such, most people do not have an intuitive understanding of how they work and instead seek to think of them as hierarchies. However, analysis reveals that trying to destabilize a cellular distributed network using tactics designed for hierarchies is likely to be ineffective. A secondary problem is that despite the vast quantities of information on the size, shape and structure of these networks, such, information is incomplete and possible erroneous. What is needed is a set of tools and an approach to assessing destabilization strategies in a decision context that takes these difficulties in to account and provides analysts with guidance in assessing alternative destabilization tactics. Such an approach is forwarded in this paper. In addition, initial lessons learned are discussed. The particular approach is extensible and scales well to groups composed of 1000’s of members