Using Bibliometric Data to Define and Understand Publishing Network Equity in Anesthesiology.

BACKGROUND: Anesthesiology departments and professional organizations increasingly recognize the need to embrace diverse membership to effectively care for patients, to educate our trainees, and to contribute to innovative research. 1 Bibliometric analysis uses citation data to determine the patterns of interrelatedness within a scientific community. Social network analysis examines these patterns to elucidate the network's functional properties. Using these methodologies, an analysis of contemporary scholarly work was undertaken to outline network structure and function, with particular focus on the equity of node and graph-level connectivity patterns. METHODS: Using the Web of Science, this study examines bibliographic data from 6 anesthesiology-specific journals between January 1, 2017, and August 26, 2022. The final data represent 4453 articles, 19,916 independent authors, and 4436 institutions. Analysis of coauthorship was performed using R libraries software. Collaboration patterns were assessed at the node and graph level to analyze patterns of coauthorship. Influential authors and institutions were identified using centrality metrics; author influence was also cataloged by the number of publications and highly cited papers. Independent assessors reviewed influential author photographs to classify race and gender. The Gini coefficient was applied to examine dispersion of influence across nodes. Pearson correlations were used to investigate the relationship between centrality metrics, number of publications, and National Institutes of Health (NIH) funding. RESULTS: The modularity of the author network is significantly higher than would be predicted by chance (0.886 vs random network mean 0.340, P < .01), signifying strong community formation. The Gini coefficient indicates inequity across both author and institution centrality metrics, representing moderate to high disparity in node influence. Identifying the top 30 authors by centrality metrics, number of published and highly cited papers, 79.0% were categorized as male; 68.1% of authors were classified as White (non-Latino) and 24.6% Asian. CONCLUSIONS: The highly modular network structure indicates dense author communities. Extracommunity cooperation is limited, previously demonstrated to negatively impact novel scientific work. 2 , 3 Inequitable node influence is seen at both author and institution level, notably an imbalance of information transfer and disparity in connectivity patterns. There is an association between network influence, article publication (authors), and NIH funding (institutions). Female and minority authors are inequitably represented among the most influential authors. This baseline bibliometric analysis provides an opportunity to direct future network connections to more inclusively share information and integrate diverse perspectives, properties associated with increased academic productivity. 3 , 4.

Team composition and the ABCs of teamwork.

In teams, some combinations of people work together better than others. A large body of literature with a rich history suggests that the configuration of team member attributes, called team composition, has a fundamental influence on teamwork. Team composition shapes the emergence of affective states, behavioral processes, and cognitive states (the ABCs of teamwork), which ultimately affect how teams meet their objectives. The purpose of this article is to describe what is known about team composition and its influence on the ABCs of teamwork. We discuss what team composition is, and why it is important. We then describe key discoveries related to how team composition shapes the ABCs of teamwork. Building on what we know, we outline important directions for future research. (PsycINFO Database Record

Team Composition Issues for Future Space Exploration: A Review and Directions for Future Research.

BACKGROUND: Future space exploration, such as a mission to Mars, will require space crews to live and work in extreme environments unlike those of previous space missions. Extreme conditions such as prolonged confinement, isolation, and expected communication time delays will require that crews have a higher level of interpersonal compatibility and be able to work autonomously, adapting to unforeseen challenges in order to ensure mission success. Team composition, or the configuration of member attributes, is an important consideration for maximizing crewmember well-being and team performance. METHODS: We conducted an extensive search to find articles about team composition in long-distance space exploration (LDSE)-analogue environments, including a search of databases, specific relevant journals, and by contacting authors who publish in the area. RESULTS: We review the team composition research conducted in analogue environments in terms of two paths through which team composition is likely to be related to LDSE mission success, namely by 1) affecting social integration, and 2) the team processes and emergent states related to team task completion. DISCUSSION: Suggestions for future research are summarized as: 1) the need to identify ways to foster unit-level social integration within diverse crews; 2) the missed opportunity to use team composition variables as a way to improve team processes, emergent states, and task completion; and 3) the importance of disentangling the effect of specific team composition variables to determine the traits (e.g., personality, values) that are associated with particular risks (e.g., subgrouping) to performance.