ArticleMedicine2020
Whether productive authors using the national health insurance database also achieve higher individual research metrics: A bibliometric study.
Article in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Pooled it
- A comprehensive approach for clustering analysis using follower-leading clustering algorithm (FLCA): Bibliometric analysis.Medicine · 2023Article
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- A Bibliometric Analysis on Dengue Outbreaks in Tropical and Sub-Tropical Climates Worldwide Since 1950.International journal of environmental research and public health · 2021Article
- Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development.International journal of environmental research and public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMany researchers use the National Health Insurance Research Database (HIRD) to publish medical papers and gain exceptional outputs in academics. Whether they also obtain excellent citation metrics remains unclear.
methodsWe searched the PubMed database (http://www.ncbi.nlm.nih.gov/pubmed) using the terms Taiwan and HIRD. We then downloaded 1997 articles published from 2012 to 2016. An authorship-weighted scheme (AWS) was applied to compute coauthor partial contributions from the article bylines. Both modified x-index and author impact factor (AIF) proved complementary to Hirsch's h-index for calculating individual research achievements (IRA). The metrics from 4684 authors were collected for comparison. Three hundred eligible authors with higher x-indexes were located and displayed on Google Maps dashboards. Ten separate clusters were identified using social network analysis (SNA) to highlight the research teams. The bootstrapping method was used to examine the differences in metrics among author clusters. The Kano model was applied to classify author IRAs into 3 parts.
resultsThe most productive author was Investigator#1 (Taichung City, Taiwan), who published 149 articles in 2015 and included 803 other members in his research teams. The Kano diagram results did not support his citation metrics beyond other clusters and individuals in IRAs.
conclusionThe AWS-based bibliometric metrics make individual weighted research evaluations possible and available for comparison. The study results of productive authors using HIRD did not support the view that higher citation metrics exist in specific disciplines.
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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.