Journals were central to Eugene Garfield's research interests. Among other things, journals are considered as units of analysis for bibliographic databases such as the Web of Science and Scopus. In addition to providing a basis for disciplinary classifications of journals, journal citation patterns span networks across boundaries to variable extents. Using betweenness centrality (BC) and diversity, we elaborate on the question of how to distinguish and rank journals in terms of interdisciplinarity. Interdisciplinarity, however, is difficult to operationalize in the absence of an operational definition of disciplines; the diversity of a unit of analysis is sample-dependent. BC can be considered as a measure of multi-disciplinarity. Diversity of co-citation in a citing document has been considered as an indicator of knowledge integration, but an author can also generate trans-disciplinary-that is, non-disciplined-variation by citing sources from other disciplines. Diversity in the bibliographic coupling among citing documents can analogously be considered as diffusion or differentiation of knowledge across disciplines. Because the citation networks in the cited direction reflect both structure and variation, diversity in this direction is perhaps the best available measure of interdisciplinarity at the journal level. Furthermore, diversity is based on a summation and can therefore be decomposed; differences among (sub)sets can be tested for statistical significance. In the appendix, a general-purpose routine for measuring diversity in networks is provided.
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http://dx.doi.org/10.1007/s11192-017-2528-2 | DOI Listing |
BMC Public Health
December 2024
Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
Background: Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases.
View Article and Find Full Text PDFChemosphere
December 2024
State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, Heilongjiang Province, 150090, PR China.
This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal).
View Article and Find Full Text PDFWorld Neurosurg
December 2024
Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India. Electronic address:
World Neurosurg
December 2024
Independent Researcher, Miki-cho, Japan. Electronic address:
Maturitas
December 2024
Applied Health Sciences, School of Health Sciences, College of Medicine and Health, University of Birmingham, Birmingham, United Kingdom. Electronic address:
Objectives: To evaluate the impact of wearable devices when associated with usual care on the incidence of major adverse cardiovascular events (MACE) in patients with ischemic heart disease compared with usual care alone.
Methods: Randomised clinical trials with patients aged 18 years and above with ischemic heart disease, using wearable devices and assessing at least one of the primary outcomes (myocardial infarction, stroke, cardiovascular mortality, or major adverse cardiovascular events) or secondary outcomes (all-cause mortality, hospitalisation, all arrhythmias, heart failure, unstable angina or revascularisation procedures) were included. MEDLINE, EMBASE, Cochrane Library, CINHAL, INAHTA and the Web of Science Core Collection were searched in April 2024.
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