Date on Master's Thesis/Doctoral Dissertation


Document Type

Doctoral Dissertation

Degree Name

Ph. D.


Health Promotion and Behavioral Sciences

Committee Chair

Carrico, Ruth Lynne

Author's Keywords

Infection prevention; Healthcare; Social network; Centrality; Geography; Healthcare-associated infections


Hospitals--Employees--Training of--Kentucky; Nosocomial infections--Prevention; Cross infection--Prevention


Background: Infection preventionists (IPs) have a multitude of tasks aimed at the prevention and control of infections in the healthcare setting. These tasks require a great deal of knowledge that has been more challenging to gain over the past decade due to the rapidly changing healthcare environment, the IPs' increasing numbers of duties, limited staffing, and a number of other issues. Because of these challenges, other mechanisms of rapid and efficient knowledge acquisition are needed for optimal job performance. One possible mechanism is knowledge sharing through social or professional networks. Objective: To examine the knowledge-sharing network structure of hospital-based IPs in Kentucky. Methods: An electronic survey instrument was e-mailed to all hospital-based IPs in Kentucky. Roster lists were used to elicit alters for knowledge sharing. Basic demographics and employment data were collected. Directed sociograms were utilized to visually examine the network. Density and component analyses were used to evaluate network cohesion. In and out-degree, betweenness, and eigenvector statistics were calculated to examine node centrality. Key player reach and fragmentation algorithms were used to identify key players. Geospatial network analysis was also used to analyze the network structure. Results: A total of 75 IPs completed the survey for a 58% response rate. Seven IPs were excluded due to their limited focus on infection prevention activities. The network density was ,1.8%. Three network components were identified. The median (range) centrality measures were as follows: in-degree, 2 (0-11); out-degree, 0.5 (0-5); betweenness, 0 (0-567); and eigenvector 0.02 (0-0.45). One IP had the highest centrality measures. Three key players were identified in the reach and fragmentation analyses, of which most were in the age range that would soon qualify them for retirement. Geospatial analysis of the network revealed that it spanned the entire state of Kentucky and did not fit into any particular sectioning of the state (Medical Trade Area, APIC chapter, physical barriers, etc.). Conclusions: Very low network density and centrality statistics indicate that the knowledge-sharing network of hospital-based IPs in Kentucky is not adequate for optimal knowledge sharing. In a state such as Kentucky with predominantly small, rural facilities that may have limited access to knowledge as compared to large, university settings, an optimal knowledge-sharing network among these facilities may allow for diffusion of new information to IPs at these facilities. Future research is needed to identify interventions to increase network connections in this field.