ArticlePublic health reports (Washington, D.C. : 1974)
Co-Authorship Network Analysis of Prevention Research Centers: An Exploratory Study.
Article in Public health reports (Washington, D.C. : 1974). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Bibliometric and social network analysis of a Clinical and Translational Resource awardee: An Oklahoma experience 2014-2021.Journal of clinical and translational science · 2024Article
- Patterns of collaboration and knowledge generated by an Australian rural research centre over 20 years: a co-authorship network analysis.Health research policy and systems · 2023Article
- Developing Social Entrepreneurship Orientation: The Impact of Internal Work Locus of Control and Bricolage.Frontiers in psychology · 2022Article
- Anaemia in Children and Adolescents: A Bibliometric Analysis of BRICS Countries (1990-2020).International journal of environmental research and public health · 2021Article
- Social Entrepreneurship Orientation and Enterprise Fortune: An Intermediary Role of Social Performance.Frontiers in psychology · 2021Article
- Prevention Research Centers and COVID-19: Models of a Community-Engaged Response to a Public Health Emergency.Public health reports (Washington, D.C. : 1974)Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe Centers for Disease Control and Prevention's Prevention Research Centers (PRCs) collaborate on public health activities with community agencies and organizations. We evaluated these collaborations by studying the relationships between co-authors from the PRCs and community agencies that published at least 1 article together in the first year of the program.
methodsWe identified all the authors of articles published by PRCs and collaborating members in peer-reviewed journals between September 2014 and September 2015 and constructed a network showing the links between and among all the authors. We characterized the network with 4 measures of social structure (network components, network density, average clustering coefficient, average distance) and 3 measures of individual author performances (degree-, betweenness-, and closeness-centrality).
resultsThe 413 articles had 1804 individual authors and 7995 co-authorship relationships (links) in 212 peer-reviewed journals. These authors and co-authors formed 44 separate, nonoverlapping groups (components). The largest "giant" component containing most of the links involved 66.3% (n = 1196) of the authors and 73.7% (n = 5889) of the links. We identified 136 "information brokers" (authors with high closeness centrality: those who have the shortest links to the most authors). Two authors with high betweenness centrality (who had the highest number of co-authors; 104 and 107) had the greatest ability to mediate co-authorships. Network density was low; only 0.5% of all potential co-authorships were realized (7995 actual co-authorship/1 628 110 potential co-authorships).
conclusionInformation brokers and co-authorship mediators should be encouraged to communicate more with each other to increase the number of collaborations between network members and, hence, the number of co-authorships.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.