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• Nine network metrics exhibited stability over seven months in an Information-Sharing and an Advice Network. • Four network metrics, Hierarchy, Fragmentation, Isolate Count, and Clique Count, exhibited instability. • Slight differences were found between day and night shifts in the Information-Sharing and the Advice Networks. 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. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research is to (1) investigate the impact of nursing unit design on nursing staff communication patterns and, ultimately, on patient falls in acute care nursing units; and (2) evaluate whether differences in fall rates, if found, were associated with the nursing unit physical structure (shape) or size. Background: Nursing staff communication and nursing unit design are frequently linked to patient safety outcomes, yet little is known about the impact of specific nursing unit designs on nursing communication patterns that might affect patient falls. Method: An exploratory longitudinal correlational design was used to measure nursing unit communication structures using social network analysis techniques. Data were collected 4 times over a 7-month period. Floor plans were used to determine nursing unit design. Fall rates were provided by hospital coordinators. Results: An analysis of covariance controlling for hospitals resulted in a statistically significant interaction of unit shape and size (number of beds). The interaction occurred when medium- and large-sized racetrack-shaped units intersected with medium- and large-sized cross-shaped units. Conclusion: The results suggest that nursing unit design shape impacts nursing communication patterns, and the interaction of shape and size may impact patient falls. How those communication patterns affect patient falls should be considered when planning hospital construction of nursing care units. [ABSTRACT FROM AUTHOR]