Visualization of the Scholarly Output on Evidence Based Librarianship: A Social Network Analysis

Abstract

Abstract
 Objective – This paper aimed to analyze worldwide research on evidence based librarianship (EBL) using Social Network Analysis (SNA).
 Methods – This descriptive study has been conducted using scientometrics and a SNA approach. The researchers identified 523 publications on EBL, as indexed by Scopus and Web of Science with no date limitation. A range of software tools (Ravar PreMap, Netdraw, UCINet and VOSviewer) were utilized for data visualization and analysis.
 Results – Results of the study revealed that the United Kingdom (UK) and the United States (US) occupied the topmost positions regarding centrality measures, clearly indicating their important structural roles in EBL research. The network of EBL research in terms of the degree of connectedness showed low density in the co-authorship networks of both authors (0.013) and countries (0.214). Seven subject clusters were identified in the EBL research network, four of which related to health and medicine. The occurrence of the keywords related to these four subject clusters suggested that EBL research had a greater association with the setting of health and medicine than with traditional librarianship elements such as human resources or library collection management.
 Conclusion – This study provided a systematic understanding of topics, research, and researchers in EBL by visualizing the networks and may thus inform the development of future aspects of EBL research and education.

Visualization of clinical teaching citations using social network analysis

“…Network density de nes as the ratio of possible ties which are recognized among the participants of network and it measures the degree consistency of the nodes. The density is scored between zero and one, being closer to one indicates that the network is more coherent and the relations between nodes are denser (13)(14)(15). The Clustering coe cient, is de ned as the ratio of the numbers of links around a node and possible links in the network.…”

“…Authors/countries with a high degree centrality can be considered to be active members and have a higher number of corporations with other nodes in the network. Indeed a network member with a higher degree is a popular actor of network and has more ability to in uence others (15,17,18).…”

“…This indicator emphasizes the structural position of nodes in the network and is de ned as total geodesic distances to all other nodes (17). Accordingly, authors with high closeness centrality are actors of the network, that receive information quickly and provide its distribution and accessibility throughout the network (14,15).…”

“…In SNA, the variations of names and nomenclatures lead to redundancy in nodes and clusters. In addition to our unique method, we used the data preparation and cleansing methods introduced by Vahed et al (25). We considered the whole list and distinct words that needed modi cation.…”

“…We also uni ed different format of country names such as, the USA and the United States of America and the US by accepted formats in the research team. Standardization of topics performed by the use of MeSH terms (Medical Subject Headings) and selected rational topic for speci c topics have not equal MeSH terms Also, we deleted numbers or symbols such as %, #,&, >," and merged single and plural forms of keywords, converted abbreviations to full form by checking them with a list of synonyms (15). Two of the researchers independently revised the list of names, nomenclatures and words.…”