A Large-Scale Analysis of Impact Factor Biased Journal Self-Citations.
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ABSTRACT:
SUBMITTER: Chorus C
PROVIDER: S-EPMC4999059 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ImportanceWomen are less likely to be promoted and hold leadership positions in academic medicine. How often academic articles are cited is a key measure of scholarly impact and frequently assessed for professional advancement; however, it is unknown whether peer-reviewed articles written by men and women are cited differently.ObjectiveTo evaluate whether academic articles from high-impact medical journals written by men and women are cited differently.Design, setting, and participantsIn this cross-sectional study of all original research and commentary articles from 5 high-impact medical journals (Annals of Internal Medicine, British Medical Journal, JAMA, JAMA Internal Medicine, and The New England Journal of Medicine) from 2015 to 2018, the gender of the primary and senior authors of each article were identified using an online database, and the number of times each article has been cited was identified using Web of Science. The number of citations by primary and senior author gender were then compared. Data were analyzed from July 2020 to April 2021.ExposuresPrimary and senior authors' genders.Main outcomes and measuresNumber of citations per article.ResultsAmong 5554 articles, women wrote 1975 (35.6%) as primary author and 1273 of 4940 (25.8%) as senior author. Original research articles written by women as primary authors had fewer median (interquartile range) citations than articles written by men as primary authors (36 [17-82] citations vs 54 [22-141] citations; P < .001) and senior authors (37 [17-93] citations vs 51 [20-128] citations; P < .001). Articles written by women as both primary and senior authors had approximately half as many median (interquartile range) citations as those authored by men as both primary and senior authors (33 [15-68] citations vs 59 [23-149] citations; P < .001). Differences in citations remained in each year of the study and were less pronounced among commentary articles.Conclusions and relevanceIn this study, articles written by women in high-impact medical journals had fewer citations than those written by men, particularly when women wrote together as primary and senior authors. These differences may have important consequences for the professional success of women and achieving gender equity in academic medicine.
| S-EPMC8254129 | biostudies-literature
Project description:Background Citations of papers are positively influenced by the journal's impact factor (IF). For non-open access (non-OA) journals, this influence may be due to the fact that high-IF journals are more often purchased by libraries, and are therefore more often available to researchers, than low-IF journals. This positive influence has not, however, been shown specifically for papers published in open access (OA) journals, which are universally accessible, and do not need library purchase. It is therefore important to ascertain if the IF influences citations in OA journals too. Methods 203 randomized controlled trials (102 OA and 101 non-OA) published in January 2011 were included in the study. Five-year citations for papers published in OA journals were compared to those for non-OA journals. Source papers were derived from PubMed. Citations were retrieved from Web of Science, Scopus, and Google Scholar databases. The Thompson-Reuter's IF was used. Results OA journals were found to have significantly more citations overall compared to non-OA journals (median 15.5 vs 12, p=0.039). The IF did not correlate with citations for OA journals (Spearman's rho =0.187, p=0.60). The increase in the citations with increasing IF was minimal for OA journals (beta coefficient = 3.346, 95% CI -0.464, 7.156, p=0.084). In contrast, the IF did show moderate correlation with citations for articles published in non-OA journals (Spearman's rho=0.514, p<0.001). The increase in the number of citations was also significant (beta coefficient = 4.347, 95% CI 2.42, 6.274, p<0.001). Conclusion It is better to publish in an OA journal for more citations. It may not be worth paying high publishing fees for higher IF journals, because there is minimal gain in terms of increased number of citations. On the other hand, if one wishes to publish in a non-OA journal, it is better to choose one with a high IF.
| S-EPMC5464220 | biostudies-literature
Project description:Citations for randomized controlled trials (RCT) are important for the dissemination of study results. However, predictors of citations for RCTs have not been investigated. The study aimed to investigate the predictors of citations for RCTs in sepsis literature. RCTs that investigated the efficacy of treatment strategies on clinical outcomes in sepsis patients were included, and publication dates were restricted to the period from 2000 to 2016. Risk of bias was assessed using the Cochrane handbook for systematic reviews and interventions. A multivariable linear regression model was built to investigate the independent variables associated with total citations. In total, 160 RCTs met our inclusion criteria and were included for analysis. The median of total citations was 28.5 (IQR: 6-76). The journal impact factor (IF) for articles was 6.312 (IQR: 3.143-7.214). The dependent variable was transformed by the square root to improve normality and meet the assumption of homoscedasticity. The journal IF (coefficient: 0.2; 95% CI: 0.16, 0.25) was independently associated with total citations. Large samples were associated with more total citations (coefficient: 0.0026; 95% CI: 0.0013, 0.0039). The study demonstrated that the journal IF was a major determinant of the RCT's total citation number.
| S-EPMC5207738 | biostudies-other
Project description: Not available
| S-EPMC10522580 | biostudies-literature
Project description:This study estimates the effect of data sharing on the citations of academic articles, using journal policies as a natural experiment. We begin by examining 17 high-impact journals that have adopted the requirement that data from published articles be publicly posted. We match these 17 journals to 13 journals without policy changes and find that empirical articles published just before their change in editorial policy have citation rates with no statistically significant difference from those published shortly after the shift. We then ask whether this null result stems from poor compliance with data sharing policies, and use the data sharing policy changes as instrumental variables to examine more closely two leading journals in economics and political science with relatively strong enforcement of new data policies. We find that articles that make their data available receive 97 additional citations (estimate standard error of 34). We conclude that: a) authors who share data may be rewarded eventually with additional scholarly citations, and b) data-posting policies alone do not increase the impact of articles published in a journal unless those policies are enforced.
| S-EPMC6919593 | biostudies-literature
Project description:Analyses of the Journal Impact Factor (JIF) have grown to be a major topic in scientometric literature. Despite widespread and justified critique concerning the JIF and its application, the size of a journal as a predictor for its longitudinal variability-or stability-on a long-term level has not yet comprehensively been analyzed. This study aims to provide robust evidence for an association between JIF variability and the size of journals, expressed by the number of published articles (citable items). For this purpose, the complete set of journals included in the Incite Journal Citation Reports (JCR) with an JIF in the 2017 JCR edition (n = 8750) were analyzed for the association between journal size and longitudinal JIF dynamics. Our results, based on n = 4792 journals with a complete JIF data set over the timespan of 12 annual JIF changes show that larger journals publishing more citable items experience smaller annual changes of the JIF than smaller journals, yet with this association being reversed for journals with a very large number of total cites. Consequently and in accordance with the genuine intention of the JIF to serve as a basis for decisions on journal subscriptions, evaluation of current changes of the JIF have to be accompanied by consideration of the journal's size in order to be accurate and sensible.
| S-EPMC6874322 | biostudies-literature
Project description:As an important biomedical database, PubMed provides users with free access to abstracts of its documents. However, citations between these documents need to be collected from external data sources. Although previous studies have investigated the coverage of various data sources, the quality of citations is underexplored. In response, this study compares the coverage and citation quality of five freely available data sources on 30 million PubMed documents, including OpenCitations Index of CrossRef open DOI-to-DOI citations (COCI), Dimensions, Microsoft Academic Graph (MAG), National Institutes of Health’s Open Citation Collection (NIH-OCC), and Semantic Scholar Open Research Corpus (S2ORC). Three gold standards and five metrics are introduced to evaluate the correctness and completeness of citations. Our results indicate that Dimensions is the most comprehensive data source that provides references for 62.4% of PubMed documents, outperforming the official NIH-OCC dataset (56.7%). Over 90% of citation links in other data sources can also be found in Dimensions. The coverage of MAG, COCI, and S2ORC is 59.6%, 34.7%, and 23.5%, respectively. Regarding the citation quality, Dimensions and NIH-OCC achieve the best overall results. Almost all data sources have a precision higher than 90%, but their recall is much lower. All databases have better performances on recent publications than earlier ones. Meanwhile, the gaps between different data sources have diminished for the documents published in recent years. This study provides evidence for researchers to choose suitable PubMed citation sources, which is also helpful for evaluating the citation quality of free bibliographic databases.
| S-EPMC8542188 | biostudies-literature
Project description:The severity and prevalence of many diseases are known to differ between the sexes. Organ specific sex-biased gene expression may underpin these and other sexually dimorphic traits. To further our understanding of sex differences in transcriptional regulation, we performed meta-analyses of sex biased gene expression in multiple human tissues. We analyzed 22 publicly available human gene expression microarray data sets including over 2500 samples from 15 different tissues and 9 different organs. Briefly, by using an inverse-variance method we determined the effect size difference of gene expression between males and females. We found the greatest sex differences in gene expression in the brain, specifically in the anterior cingulate cortex, (1818 genes), followed by the heart (375 genes), kidney (224 genes), colon (218 genes), and thyroid (163 genes). More interestingly, we found different parts of the brain with varying numbers and identity of sex-biased genes, indicating that specific cortical regions may influence sexually dimorphic traits. The majority of sex-biased genes in other tissues such as the bladder, liver, lungs, and pancreas were on the sex chromosomes or involved in sex hormone production. On average in each tissue, 32% of autosomal genes that were expressed in a sex-biased fashion contained androgen or estrogen hormone response elements. Interestingly, across all tissues, we found approximately two-thirds of autosomal genes that were sex-biased were not under direct influence of sex hormones. To our knowledge this is the largest analysis of sex-biased gene expression in human tissues to date. We identified many sex-biased genes that were not under the direct influence of sex chromosome genes or sex hormones. These may provide targets for future development of sex-specific treatments for diseases.
| S-EPMC5062749 | biostudies-literature
Project description:To correct the incongruence of document types between the numerator and denominator in the traditional impact factor (IF), we make a corresponding adjustment to its formula and present five corrective IFs: IFTotal/Total, IFTotal/AREL, IFAR/AR, IFAREL/AR, and IFAREL/AREL. Based on a survey of researchers in the fields of ophthalmology and mathematics, we obtained the real impact ranking of sample journals in the minds of peer experts. The correlations between various IFs and questionnaire score were analyzed to verify their journal evaluation effects. The results show that it is scientific and reasonable to use five corrective IFs for journal evaluation for both ophthalmology and mathematics. For ophthalmology, the journal evaluation effects of the five corrective IFs are superior than those of traditional IF: the corrective effect of IFAR/AR is the best, IFAREL/AR is better than IFTotal/Total, followed by IFTotal/AREL, and IFAREL/AREL. For mathematics, the journal evaluation effect of traditional IF is superior than those of the five corrective IFs: the corrective effect of IFTotal/Total is best, IFAREL/AR is better than IFTotal/AREL and IFAREL/AREL, and the corrective effect of IFAR/AR is the worst. In conclusion, not all disciplinary journal IF need correction. The results in the current paper show that to correct the IF of ophthalmologic journals may be valuable, but it seems to be meaningless for mathematic journals.
| S-EPMC4792445 | biostudies-literature
Project description:BackgroundPublications regarding the 100 top-cited articles in a given discipline are common, but studies reporting the association between article topics and their citations are lacking. Whether or not reviews and original articles have a higher impact factor than case reports is a point for verification in this study. In addition, article topics that can be used for predicting citations have not been analyzed. Thus, this study aims to METHODS:: We searched PubMed Central and downloaded 100 top-cited abstracts in the journal Medicine (Baltimore) since 2011. Four article types and 7 topic categories (denoted by MeSH terms) were extracted from abstracts. Contributors to these 100 top-cited articles were analyzed. Social network analysis and Sankey diagram analysis were performed to identify influential article types and topic categories. MeSH terms were applied to predict the number of article citations. We then examined the prediction power with the correlation coefficients between MeSH weights and article citations.ResultsThe citation counts for the 100 articles ranged from 24 to 127, with an average of 39.1 citations. The most frequent article types were journal articles (82%) and comparative studies (10%), and the most frequent topics were epidemiology (48%) and blood and immunology (36%). The most productive countries were the United States (24%) and China (23%). The most cited article (PDID = 27258521) with a count of 135 was written by Dr Shang from Shandong Provincial Hospital Affiliated to Shandong University (China) in 2016. MeSH terms were evident in the prediction power of the number of article citations (correlation coefficients = 0.49, t = 5.62).ConclusionThe breakthrough was made by developing dashboards showing the overall concept of the 100 top-cited articles using the Sankey diagram. MeSH terms can be used for predicting article citations. Analyzing the 100 top-cited articles could help future academic pursuits and applications in other academic disciplines.
| S-EPMC7598835 | biostudies-literature