Applied Sciences (Jul 2023)

Altmetric Behaviour over a Two-Year Observation Period: A Longitudinal Cohort Study in Orthodontic Research

  • Daniele Garcovich,
  • Angel Zhou Wu,
  • Carolina Soledad Romero García,
  • Alfonso Alvarado Lorenzo,
  • Riccardo Aiuto,
  • Milagros Adobes Martin

DOI
https://doi.org/10.3390/app13148404
Journal volume & issue
Vol. 13, no. 14
p. 8404

Abstract

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Background: Alternative metrics have been proposed to estimate the impact of research on the academic and social environment. The objective of the current study was to analyze the longitudinal behavior of Altmetric resources related to online engagement in orthodontic research and to explore their correlation with citations over time. Methods: The Dimensions App was searched in December 2019 and December 2021 for published items belonging to orthodontic journals listed in the Journal Citation Reports (JCR) from 2014 to 2018. Items with an AAS (Altmetric Attention Score) equal to or greater than one were selected and screened for data related to authorship and publication. The breakdown of the different Altmeric resources was collected in 2019 and updated in 2021. Citations were retrieved from Web of Science (WOS) and Scopus at the same time interval. Results: The best performing journals were Progress in Orthodontics and the European Journal of Orthodontics at both time points, with a mean AAS per published item of 1.74 and 1.63, respectively, in 2021. The topics with the highest online engagement display a change over time, while the study design remained randomized clinical trials (RCTs) in both observations. Tweets, Facebook posts, and blogs showed a very slight increase over time, while News Outlets, patent data, and policy sources longitudinally showed a significant increase. No or poor correlation was found between altmetrics and citation except for Mendeley reader count. Conclusions: Tweets, Facebook, and Blog mentions can be considered attention trackers. News Outlets, patents, and policy sources are time dependent data. Mendeley reader count, can help to identify the article with a future citation potential.

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