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Bibliometric Analysis of Manuscript Characteristics that Influence Citations: A Comparison of Six Major Family Medicine Journals

Paracha, H.; Johal, A.; Tiwana, M.; Hafeez, D. M.; Rehman, A.; Jalal, S.; Hussain, S. A.; Khosa, F.

2020-08-13 primary care research
10.1101/2020.04.13.20063719 medRxiv
Show abstract

ObjectiveThe premise of our study was to investigate the characteristics of family medicine (FM) manuscripts that influence citation rate, capturing features of manuscript construction that are discrete from the study design. DesignWe conducted a cross-sectional study of published articles (n = 199), from January to June 2008, from 6 major FM journals with the highest impact factor. Annals of Family Medicine (IF = 1.864), British Journal of General Practice (1.104), Journal of American Board of Family Medicine (1.015), Family Practice (0.976), Family Medicine (0.936), and BMC Family Practice (0.815). Citation counts for these articles were retrieved using Web of Science filter on SCImago and 25 article characteristics were tabulated manually. We then predicted the citation rate by performing univariate analysis, spearman rank-order correlation, and multiple regression model on the collected variables. ResultsUsing spearman rank-order correlation, we found the following variables to have significant positive correlation with citations: number of references (rs and p-value, 0.31 and 0.001 respectively), total words (0.36, 0.001), number of pages (0.33, 0.001), abstract word count (0.17, 0.010) and abstract character count (0.16, 0.010). In a multivariate linear regression model: number of references (p-value = 0.010, R2 = 0.06) and multi-institutional (p-value = 0.050, R2 = 0.01) had a significant effect on citation rates. ConclusionEditors and authors of FM can enhance the impact of their journals and articles by utilizing this bibliometric study when assembling their manuscript.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.