Exploring the Stability of Communication Network Metrics in a Dynamic Nursing Context

Area of Science:

• Social network analysis
• Organizational communication
• Healthcare management

Background:

• Social networks are dynamic and evolve over time.
• Hospital patient care units (PCUs) require network flexibility to adapt to changing environments.
• Understanding network stability is key to analyzing dynamic social processes.

Purpose of the Study:

• To identify stable metrics for analyzing communication networks in PCUs over time.
• To assess the stability of various network metrics using Coefficient of Variation.
• To investigate the impact of information confidence on network metric stability.

Main Methods:

• Assessed Across Time Stability (ATS) and Global Stability using Coefficient of Variation.
• Collected data at four time points: Baseline, 1 month, 4 months, and 7 months.
• Defined "super stable" metrics as those stable by both ATS and Global Stability methods.

Main Results:

• Nine network metrics were identified as "super stable": Node Set Size, Average Distance, Clustering Coefficient, Density, Weighted Density, Diffusion, Total Degree Centrality, Betweenness Centrality, and Eigenvector Centrality.
• Hierarchy, Fragmentation, Isolate Count, and Clique Count were found to be unstable metrics.
• High staff confidence in information maintained the stability of "super stable" metrics, while low confidence eroded it.

Conclusions:

• Nursing units function as dynamic behavior settings, necessitating constant communication adjustments.
• Observed stability in nine network metrics supports the use of network analysis for studying communication in dynamic settings like PCUs.
• Network analysis provides valuable insights into maintaining functional integrity and communication patterns within healthcare environments.