Describing nurses' communication about evidence-based practice change: A social network analysis of hospital nurses.

BACKGROUND: Nurses play a critical role in providing evidence-based, high-quality care to optimize patient outcomes. Models from implementation science suggest social networks may influence the adoption of evidence-based practices (EBPs). However, few studies have examined this relationship among hospital nurses. Social network analysis (SNA) mathematically evaluates patterns of communication, a critical step in implementation. Exploring hospital nurses' communication networks may provide insight into influences on the adoption of EBPs. AIMS: This study aimed to describe complete communication networks of hospital nurses for practice changes on inpatient units, including upper level nursing administrators. METHODS: This descriptive, exploratory, cross-sectional study used SNA on two inpatient units from one hospital. A sociometric survey was completed by nurses (unit to executive level) regarding communication frequency about practice changes. Network-level density, diameter, average path length, centralization, and arc reciprocity were measured. Attribute data were used to explore subnetworks. RESULTS: Surveys from 148 nurses on two inpatient adult intensive care units (response rates 90% and 98%) revealed high communication frequency. Network measures were similar across the two units and among subnetworks. Analysis identified central (charge nurses and nurse leaders) and peripheral members of the network (new-to-practice nurses). Subnetworks aligned with the weekend and shift worked. LINKING EVIDENCE TO ACTION: Established communication channels, including subnetworks and opinion leaders, should be used to maximize and optimize implementation strategies and facilitate the uptake of EBPs. Future work should employ SNA to measure the impact of communication networks on promoting the uptake of EBP and to improve patient outcomes.

A social network analysis to explore collaborative practice in home care: research protocol.

BACKGROUND: The conceptualization of the home as a care environment and maintaining a high standard of care requires different professionals to collaborate. This study will explore collaborative practice in home care, needs and expectations of the stakeholders involved, and identify their roles and tasks. Secondly, it will investigate possible strategies to improve home care management and, more particularly, optimize collaborative practice in home care. METHODS: The study will be conducted during three distinct consecutive phases, within a multiphase mixed-methods design. Phase 1 will use a quantitative approach in which a social network analysis will be conducted to have an overview of collaborative practice in home care in French-speaking Switzerland. Phases 2 and 3 will be qualitative and focus on three different situations involving different locations (rural and urban) and different home care functioning (home care provided by agencies and home care providing by independent caregivers). In each situation, semi-structured interviews will be conducted with home care recipients and their home caregivers. In phase 2, results of phase 1's network analysis will be discussed, such as roles, needs, and expectations of all stakeholders involved in home care. In phase 3, phase 2's findings will be discussed and strategies to improve home care and to optimize collaborative practice will be explored. DISCUSSION: Over the past years, home care has grown considerably. Therefore, more and more different caregivers are involved in the recipients' homes. Since optimal coordination between these different caregivers is a prerequisite for quality and safe care, it is essential to investigate the existing collaborative practice and how it is functioning. This study will provide knowledge on roles, needs and expectations of different caregivers involved in home care. It will also allow for strategies to optimize collaborative practice and thus ensure comprehensive care for recipients. Finally, it will serve as a basis for future studies that can be conducted to address identified needs.

Understanding factors influencing workers' unsafe behaviors through social network analysis in the mining industry.

Background. In this study, we aimed to discover why workers engage in unsafe behaviors and reach a deep understanding of all causes influencing unsafe acts among workers in a mining industry. We also aimed to determine which causes play a more important role in the occurrence of unsafe acts. Methods. First, we determined the unsafe acts through observation and then investigated the factors affecting them based on semi-structured interviews. In order to identify the most important causes, we utilized centrality indicators including the degree and betweenness centrality of the social network analysis using UCINET version 6.0. Results. In total, 3058 unsafe acts were observed and 85 causes, classified into 14 factors, were found to influence the behavioral intention of workers. In this study, inadequate supervision, with the highest degree of centrality, proved to be of great influence on performing unsafe behaviors. In addition, inadequate training, with the highest betweenness centrality, was determined as the main intermediary in the mining industry under investigation. Discussion and conclusion. The findings indicate that workers' unsafe acts are affected by different causes, leading to significant implications for policy-makers seeking to reduce unsafe behaviors and to improve workplace safety in the mining environment.

Supporting Cross-Company Networks in Workplace Health Promotion through Social Network Analysis-Description of the Methodological Approach and First Results from a Model Project on Physical Activity Promotion in Germany.

Cross-company networking and counseling is considered to be a promising approach for workplace health promotion in small and medium-sized enterprises. However, a systematic and empirical approach on how such networks can be developed is lacking. The aims of the present paper are to describe the approach of a social network analysis supporting the development of a cross-company network promoting physical activity and to present first results. In the process of developing the methodological approach, a common understanding of the nodes and edges within the project was elaborated. Based on the BIG-model as the theoretical framework of the project, five measuring points and an application-oriented data collection table were determined. Using Gephi, network size, degree, and distance measures, as well as density and clustering measures, were calculated and visualized in the course of the time. First results showed a continuous expansion and densification of the network. The application experience showed that the application of social network analysis in practical cross-company network development is promising but currently still very resource intensive. In order to address the current major challenges and enable routine application, the development of an application-oriented and feasible tool could make an essential contribution.