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Network analysis reveals nine "super stable" communication metrics in hospital patient care units. High staff confidence maintains network stability, crucial for adapting to dynamic healthcare environments.

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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.