Evaluating the dependability of reference-driven citation forecasts amid the COVID-19 pandemic: A bibliometric analysis across diverse journals

BACKGROUND

The journal impact factor significantly influences research publishing and funding decisions. With the surge in research due to COVID-19, this study investigates whether references remain reliable citation predictors during this period.

METHODS

Four multidisciplinary journals (PLoS One, Medicine [Baltimore], J. Formos. Med. Assoc., and Eur. J. Med. Res.) were analyzed using the Web of Science database for 2020 to 2022 publications. The study employed descriptive, predictive, and diagnostic analytics, with tools such as 4-quadrant radar plots, univariate regressions, and country-based collaborative maps via the follower-leading cluster algorithm.

RESULTS

Six countries dominated the top 20 affiliations: China, Japan, South Korea, Taiwan, Germany, and Brazil. References remained strong citation indicators during the COVID-19 period, except for Eur. J. Med. Res. due to its smaller sample size (n = 492) than other counterparts (i.e., 41,181, 12,793, and 1464). Three journals showed higher network density coefficients, suggesting a potential foundation for reference-based citation predictions.

CONCLUSION

Despite variations among journals, references effectively predict article citations during the COVID-19 era, underlining the importance of network density. Future studies should delve deeper into the correlation between network density and citation prediction.

A study of the correlation between publication delays and measurement indicators of journal articles in the social network environment-based on online data in PLOS

The development of network technique and open access has made numerous research results freely obtained online, thereby facilitating the growth of the emerging evaluation methods of Altmetrics. However, it is unknown whether the time interval from reception to publication has an impact on the evaluation indicators of articles in the social network environment. We construct a range of time-series indexes that represent the features of the evaluation indicators and then explore the correlation of acceptance delay, technical delay, and overall delay with the relevant indicators of citations, usage, sharing and discussions, and collections that are obtained from the open access journal platform PLOS. Moreover, this research also explores the differences in the correlations of the delays for the literature in six subject areas with the corresponding indicators and the discrepancies of the correlations of delays and indexes in various metric quartiles. The results of the Mann-Whitney U test reveal that the length of delays affects the performance of the literature on some indicators. This study indicates that reducing the acceptance time and final publication time of articles can improve the efficiency of knowledge diffusion through the formal academic citation channel, but in the context of social networking communication, an appropriate interval at a particular stage in the publishing process can enhance the heat of sharing, discussion, and collection of articles to a small extent, hence boosting the influence and attention received by the literature on the internet.

How COVID-19 Affected the Journal Impact Factor of High Impact Medical Journals: Bibliometric Analysis

BACKGROUND

Journal impact factor (IF) is the leading method of scholarly assessment in today's research world, influencing where scholars submit their research and where funders distribute their resources. COVID-19, one of the most serious health crises, resulted in an unprecedented surge of publications across all areas of knowledge. An important question is whether COVID-19 affected the gold standard of scholarly assessment.

OBJECTIVE

In this paper, we aimed to comprehensively compare the productivity trends of COVID-19 and non-COVID-19 literature as well as track their evolution and scholarly impact across 3 consecutive calendar years.

METHODS

We took as an example 6 high-impact medical journals (Annals of Internal Medicine [Annals], The British Medical Journal [The BMJ], Journal of the American Medical Association [JAMA], The Lancet, Nature Medicine [NatMed], and The New England Journal of Medicine [NEJM]) and searched the literature using the Web of Science database for manuscripts published between January 1, 2019, and December 31, 2021. To assess the effect of COVID-19 and non-COVID-19 literature in their scholarly impact, we calculated their annual IFs and percentage changes. Thereafter, we estimated the citation probability of COVID-19 and non-COVID-19 publications along with their rates of publication and citation by journal.

RESULTS

A significant increase in IF change for manuscripts including COVID-19 published from 2019 to 2020 (P=.002; Annals: 283%; The BMJ: 199%; JAMA: 208%; The Lancet: 392%; NatMed: 111%; and NEJM: 196%) and to 2021 (P=.007; Annals: 41%; The BMJ: 90%; JAMA: 6%; The Lancet: 22%; NatMed: 53%; and NEJM: 72%) was seen, against non-COVID-19 ones. The likelihood of highly cited publications was significantly increased in COVID-19 manuscripts between 2019 and 2021 (Annals: z=3.4, P<.001; The BMJ: z=4.0, P<.001; JAMA: z=3.8, P<.001; The Lancet: z=3.5, P<.001; NatMed: z=5.2, P<.001; and NEJM: z=4.7, P<.001). The publication and citation rates of COVID-19 publications followed a positive trajectory, as opposed to non-COVID-19. The citation rate for COVID-19 publications peaked by the second quarter of 2020 while that of the publication rate approximately a year later.

CONCLUSIONS

The rapid surge of COVID-19 publications emphasized the capacity of scientific communities to respond against a global health emergency, yet inflated IFs create ambiguity as benchmark tools for assessing scholarly impact. The immediate implication is a loss in value of and trust in journal IFs as metrics of research and scientific rigor perceived by academia and society. Loss of confidence toward procedures employed by highly reputable publishers may incentivize authors to exploit the publication process by monopolizing their research on COVID-19 and encourage them to publish in journals of predatory behavior.

Global Research Trends of Gender-Related Artificial Intelligence in Medicine Between 2001-2020: A Bibliometric Study

This study aimed to assess the research on medical Artificial intelligence (AI) related to sex/gender and explore global research trends over the past 20 years. We searched the Web of Science (WoS) for gender-related medical AI publications from 2001 to 2020. We extracted the bibliometric data and calculated the annual growth of publications, Specialization Index, and Category Normalized Citation Impact. We also analyzed the publication distributions by institution, author, WoS subject category, and journal. A total of 3,110 papers were included in the bibliometric analysis. The number of publications continuously increased over time, with a steep increase between 2016 and 2020. The United States of America and Harvard University were the country and institution that had the largest number of publications. Surgery and urology nephrology were the most common subject categories of WoS. The most occurred keywords were machine learning, classification, risk, outcomes, diagnosis, and surgery. Despite increased interest, gender-related research is still low in medical AI field and further research is needed.