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Social network analysis of a scientist–practitioner research initiative established to facilitate science dissemination and implementation within states and communities
Research Evaluation ( IF 2.800 ) Pub Date : 2017-08-14 , DOI: 10.1093/reseval/rvx026
Elizabeth M Ginexi 1 , Grace Huang 2 , Michael Steketee 2 , Sophia Tsakraklides 2 , Keith MacAllum 2 , Julie Bromberg 3 , Amanda Huffman 3 , Douglas A Luke 4 , Scott J Leischow 5 , Janet M Okamoto 5 , Todd Rogers 6
Affiliation  

This article presents a case study of a scientist–practitioner research network established by the National Cancer Institute’s State and Community Tobacco Control Research Initiative. While prior programs have focused on collaboration among scientists, a goal here was to encourage collaborations with non-university, practice-based partners. Two stages of analyses examine growth in the network and collaboration outcomes over a 2-year timeframe. First, visual and descriptive analyses were used to assess the network’s structure and characteristics. Second, regression modeling was used to assess the relationship between investigator characteristics on active collaboration with non-university partners in research and coauthorship. Network analysis revealed an increasing number of connections, low and decreasing density, increasing centralization and select individuals with high degree and betweenness centralities. Investigator seniority and experience did not predict the active partner connections. Rather, scientists’ betweenness centrality, or the extent to which they acted as bridges across the network, was the key predictor of collaboration. This finding suggests a novel way for dissemination-focused research programs to identify super-connector investigators to foster practitioner linkages.

中文翻译:

为促进州和社区内的科学传播和实施而建立的科学家-从业者研究计划的社交网络分析

本文介绍了由国家癌症研究所的州和社区烟草控制研究计划所建立的科学家-从业者研究网络的案例研究。尽管先前的计划着重于科学家之间的合作,但此处的目标是鼓励与非大学,基于实践的合作伙伴进行合作。分析的两个阶段检查了两年内网络的增长和协作成果。首先,使用视觉和描述性分析来评估网络的结构和特征。其次,回归模型用于评估研究人员与非大学合作伙伴在研究和共同著作中的积极合作之间的关系。网络分析表明,连接数量不断增加,密度越来越低,提高集中度,并选择具有高度中间性的个人。研究者的资历和经验无法预测活跃的合作伙伴关系。相反,科学家之间的中间性或他们在整个网络中充当桥梁的程度,是协作的关键预测指标。这一发现为以传播为重点的研究计划提供了一种新颖的方法,可以确定超级连接者调查员以促进从业者之间的联系。
更新日期:2017-08-14
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