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Cross-sectional analysis of bibliometrics and altmetrics: comparing the impact of qualitative and quantitative articles in the British Medical Journal.

ABSTRACT: OBJECTIVES:In comparison to quantitative research, the impact of qualitative articles in the medical literature has been questioned by the BMJ; to explore this, we compared the impact of quantitative and qualitative articles published in BMJ. DESIGN:Cross-sectional survey. SETTING:Articles published in the BMJ between 2007 and 2017. MAIN OUTCOME MEASURES:Bibliometric and altmetric measures of research impact were collected using Web of Science, Google Scholar, Scopus, Plum Analytics and ProQuest Altmetric. Bibliometric measures consisted of citation numbers, field weighted citation impact and citation percentile. Altmetric measures consisted of article usage, captures, mentions, readers, altmetric attention score and score percentile. Scores were compared using the Wilcoxon Rank-sum test. RESULTS:We screened a total of 7777 articles and identified 42 qualitative articles. Each qualitative article was matched to 3 quantitative articles published during the same year (126 quantitative articles). Citation numbers were not statistically different between the two research types; the median number of citations (google scholar) per quantitative article was 62 (IQR 38-111) versus 58 (IQR 36-85) per qualitative article (p=0.47). Using Plum Analytics, qualitative articles were found to have a significantly higher usage, with a median of 984 (IQR 581-1351) versus 379 (IQR 177-763) for quantitative (p<0.001). The Altmetric Attention Score was higher for quantitative articles at 16 (IQR 7-37) versus qualitative articles at 9 (IQR 5-23, p=0.05), as was the Altmetric Score percentile 93 (IQR 87-96) versus 88 (IQR 76-95; p=0.02). CONCLUSION:Qualitative and quantitative articles published in the BMJ between 2007 and 2017 both have a high impact. No article type was consistently superior in terms of bibliometric or altmetric measures, suggesting that type of article is not the major driver of impact.

SUBMITTER: Retrouvey H

PROVIDER: S-EPMC7580064 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Cross-sectional analysis of bibliometrics and altmetrics: comparing the impact of qualitative and quantitative articles in the <i>British Medical Journal</i>.

Retrouvey Helene H Webster Fiona F Zhong Toni T Gagliardi Anna R AR Baxter Nancy N NN

BMJ open 20201021 10

<h4>Objectives</h4>In comparison to quantitative research, the impact of qualitative articles in the medical literature has been questioned by the BMJ; to explore this, we compared the impact of quantitative and qualitative articles published in BMJ.<h4>Design</h4>Cross-sectional survey.<h4>Setting</h4>Articles published in the BMJ between 2007 and 2017.<h4>Main outcome measures</h4>Bibliometric and altmetric measures of research impact were collected using Web of Science, Google Scholar, Scopus ...[more]

PMID: 33087378

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Project description:BackgroundSystematic reviews are important for informing clinical practice and health policy. The aim of this study was to examine the bibliometrics of systematic reviews and to determine the amount of variance in citations predicted by the journal impact factor (JIF) alone and combined with several other characteristics.MethodsWe conducted a bibliometric analysis of 1,261 systematic reviews published in 2008 and the citations to them in the Scopus database from 2008 to June 2012. Potential predictors of the citation impact of the reviews were examined using descriptive, univariate and multiple regression analysis.ResultsThe mean number of citations per review over four years was 26.5 (SD ± 29.9) or 6.6 citations per review per year. The mean JIF of the journals in which the reviews were published was 4.3 (SD ± 4.2). We found that 17% of the reviews accounted for 50% of the total citations and 1.6% of the reviews were not cited. The number of authors was correlated with the number of citations (r = 0.215, P < 0.001). Higher numbers of citations were associated with the following characteristics: first author from the United States (36.5 citations), an ICD-10 chapter heading of Neoplasms (31.8 citations), type of intervention classified as Investigation, Diagnostics or Screening (34.7 citations) and having an international collaboration (32.1 citations). The JIF alone explained more than half of the variation in citations (R(2) = 0.59) in univariate analysis. Adjusting for both JIF and type of intervention increased the R2 value to 0.81. Fourteen percent of reviews published in the top quartile of JIFs (? 5.16) received citations in the bottom quartile (eight or fewer), whereas 9% of reviews published in the lowest JIF quartile (? 2.06) received citations in the top quartile (34 or more). Six percent of reviews in journals with no JIF were also in the first quartile of citations.ConclusionsThe JIF predicted over half of the variation in citations to the systematic reviews. However, the distribution of citations was markedly skewed. Some reviews in journals with low JIFs were well-cited and others in higher JIF journals received relatively few citations; hence the JIF did not accurately represent the number of citations to individual systematic reviews.

Project description:BackgroundCluster analysis is vital in bibliometrics for deciphering large sets of academic data. However, no prior research has employed a cluster-pattern algorithm to assess the similarities and differences between 2 clusters in networks. The study goals are 2-fold: to create a cluster-pattern comparison algorithm tailored for bibliometric analysis and to apply this algorithm in presenting clusters of countries, institutes, departments, authors (CIDA), and keywords on journal articles during and after COVID-19.MethodsWe analyzed 9499 and 5943 articles from the Journal of Medicine (Baltimore) during and after COVID-19 in 2020 to 2021 and 2022 to 2023, sourced from the Web of Science (WoS) Core Collection. Follower-leading clustering algorithm (FLCA) was compared to other 8 counterparts in cluster validation and effectiveness and a cluster-pattern-comparison algorithm (CPCA) was developed using the similarity coefficient, collaborative maps, and thematic maps to evaluate CIDA cluster patterns. The similarity coefficients were categorized as identical, similar, dissimilar, or different for values above 0.7, between 0.5 and 0.7, between 0.3 and 0.5, and below 0.3, respectively.ResultsBoth stages displayed similar trends in annual publications and average citations, although these trends are decreasing. The peak publication year was 2020. Similarity coefficients of cluster patterns in these 2 stages for CIDA entities and keywords were 0.73, 0.35, 0.80, 0.02, and 0.83, respectively, suggesting the existence of identical patterns (>0.70) in countries, departments, and keywords plus, but dissimilar (<0.5) and different patterns (<0.3) found in institutes and 1st and corresponding authors, during and after COVID-19.ConclusionsThis research effectively created and utilized CPCA to analyze cluster patterns in bibliometrics. It underscores notable identical patterns in country-/department-/keyword based clusters, but dissimilar and different in institute-/author- based clusters, between these 2 stages during and after COVID-19, offering a framework for future bibliographic studies to compare cluster patterns beyond just the CIDA entities, as demonstrated in this study.

Dataset Information

Cross-sectional analysis of bibliometrics and altmetrics: comparing the impact of qualitative and quantitative articles in the British Medical Journal.

Publications

Cross-sectional analysis of bibliometrics and altmetrics: comparing the impact of qualitative and quantitative articles in the <i>British Medical Journal</i>.

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