TY - JOUR
T1 - Who are the key players in a new translational research network?
AU - Long, Janet C.
AU - Cunningham, Frances C.
AU - Carswell, Peter
AU - Braithwaite, Jeffrey
N1 - Copyright the Author(s) 2013. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2013
Y1 - 2013
N2 - Background: Professional networks are used increasingly in health care to bring together members from different sites and professions to work collaboratively. Key players within these networks are known to affect network function through their central or brokerage position and are therefore of interest to those who seek to optimise network efficiency. However, their identity may not be apparent. This study using social network analysis to ask: (1) Who are the key players of a new translational research network (TRN)? (2) Do they have characteristics in common? (3) Are they recognisable as powerful, influential or well connected individuals?. Methods. TRN members were asked to complete an on-line, whole network survey which collected demographic information expected to be associated with key player roles, and social network questions about collaboration in current TRN projects. Three questions asked who they perceived as powerful, influential and well connected. Indegree and betweenness centrality values were used to determine key player status in the actual and perceived networks and tested for association with demographic and descriptive variables using chi square analyses. Results: Response rate for the online survey was 76.4% (52/68). The TRN director and manager were identified as key players along with six other members. Only two of nine variables were associated with actual key player status; none with perceived. The main finding was the mismatch between actual and perceived brokers. Members correctly identified two of the three central actors (the two mandated key roles director and manager) but there were only three correctly identified actual brokers among the 19 perceived brokers. Possible reasons for the mismatch include overlapping structures and weak knowledge of members. Conclusions: The importance of correctly identifying these key players is discussed in terms of network interventions to improve efficiency.
AB - Background: Professional networks are used increasingly in health care to bring together members from different sites and professions to work collaboratively. Key players within these networks are known to affect network function through their central or brokerage position and are therefore of interest to those who seek to optimise network efficiency. However, their identity may not be apparent. This study using social network analysis to ask: (1) Who are the key players of a new translational research network (TRN)? (2) Do they have characteristics in common? (3) Are they recognisable as powerful, influential or well connected individuals?. Methods. TRN members were asked to complete an on-line, whole network survey which collected demographic information expected to be associated with key player roles, and social network questions about collaboration in current TRN projects. Three questions asked who they perceived as powerful, influential and well connected. Indegree and betweenness centrality values were used to determine key player status in the actual and perceived networks and tested for association with demographic and descriptive variables using chi square analyses. Results: Response rate for the online survey was 76.4% (52/68). The TRN director and manager were identified as key players along with six other members. Only two of nine variables were associated with actual key player status; none with perceived. The main finding was the mismatch between actual and perceived brokers. Members correctly identified two of the three central actors (the two mandated key roles director and manager) but there were only three correctly identified actual brokers among the 19 perceived brokers. Possible reasons for the mismatch include overlapping structures and weak knowledge of members. Conclusions: The importance of correctly identifying these key players is discussed in terms of network interventions to improve efficiency.
UR - http://www.scopus.com/inward/record.url?scp=84883200318&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/nhmrc/568612
UR - http://purl.org/au-research/grants/arc/DP0986493
U2 - 10.1186/1472-6963-13-338
DO - 10.1186/1472-6963-13-338
M3 - Article
C2 - 23987790
AN - SCOPUS:84883200318
SN - 1472-6963
VL - 13
SP - 1
EP - 11
JO - BMC Health Services Research
JF - BMC Health Services Research
M1 - 338
ER -