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Updated: Mar 21, 2026
Using Learning Outcome Measures to assess Doctoral Nursing Education
Published on: June 21, 2010
Network analysis of personality-capital-competence interrelationships among nursing master's and doctoral
1School of Nursing, Zhejiang Chinese Medical University, No. 548 Binwen Road, Binjiang District, Hangzhou, 310053, China.
Background:
Graduate nursing education requires effective supervisory competence to prepare advanced practice nurses and nursing scientists. While personality traits and psychological capital influence supervisory effectiveness, the complex interrelationships among these psychological constructs and specific supervisory competencies remain inadequately understood through traditional analytical approaches.
Objective:
This study examined the component-to-component network structure connecting Big Five personality traits, psychological capital, and nursing supervisory competence dimensions among graduate nursing supervisors, identifying key bridge nodes and providing systems-level understanding of psychological mechanisms underlying supervisory effectiveness.
Design:
Cross-sectional network analysis study employing Gaussian Graphical Models.
Setting:
Various Chinese universities offering nursing graduate programs and their affiliated clinical institutions across Eastern, Central, and Western China.
Participants:
294 nursing graduate supervisors were recruited through convenience sampling from July to September 2025.
Methods:
Data collection utilized validated instruments measuring Big Five personality traits, psychological capital, and supervisory instructional abilities. Network analysis employed Gaussian Graphical Models with regularization techniques, centrality indices computation, and bridge expected influence analysis to identify critical connectors between supervisory domains.
Results:
Academic guidance ability emerged as the node with highest expected influence, while hope demonstrated the strongest centrality within psychological capital. Conscientiousness exhibited the highest bridge expected influence across all constructs (0.532), with self-efficacy serving as the primary psychological capital bridge (0.363). Neuroticism displayed negative bridging effects (-0.343), indicating inhibitory influence on cross-domain connections. Network stability analysis confirmed robustness of centrality findings across different sample configurations.
Conclusions:
The network structure reveals a multilevel psychological system where stable personality traits establish foundations for developable psychological capital, which subsequently connects to concrete supervisory competencies. These findings suggest that faculty development interventions targeting key network nodes and strengthening critical bridge connections may achieve superior outcomes compared to approaches focusing on individual variables independently.
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