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Measuring the impact of scientific publications and publication extenders: examples of novel approaches.

Avishek PalWesley PortegiesJennifer SchwinnMichael TaylorTomas James ReesSarah ThomasKim BrownGareth MorrellJoshua M NicholsonBrian FalconeRenu Juneja
Published in: Current medical research and opinion (2024)
Different stakeholders, such as authors, research institutions, and healthcare professionals (HCPs) may determine the impact of peer-reviewed publications in different ways. Commonly-used measures of research impact, such as the Journal Impact Factor or the H-index, are not designed to evaluate the impact of individual articles. They are heavily dependent on citations, and therefore only measure impact of the overall journal or researcher respectively, taking months or years to accrue. The past decade has seen the development of article-level metrics (ALMs), that measure the online attention received by an individual publication in contexts including social media platforms, news media, citation activity, and policy and patent citations. These new tools can complement traditional bibliometric data and provide a more holistic evaluation of the impact of a publication. This commentary discusses the need for ALMs, and summarizes several examples - PlumX Metrics, Altmetric, the Better Article Metrics score, the EMPIRE Index, and scite. We also discuss how metrics may be used to evaluate the value of "publication extenders" - educational microcontent such as animations, videos and plain-language summaries that are often hosted on HCP education platforms. Publication extenders adapt a publication's key data to audience needs and thereby extend a publication's reach. These new approaches have the potential to address the limitations of traditional metrics, but the diversity of new metrics requires that users have a keen understanding of which forms of impact are relevant to a specific publication and select and monitor ALMs accordingly.
Keyphrases
  • social media
  • healthcare
  • health information
  • public health
  • autism spectrum disorder
  • machine learning
  • climate change
  • data analysis