Bibliometric Analysis of Manuscript Characteristics that Influence Citations: A Comparison of Six Major Family Medicine Journals
Bibliographic record
Abstract
ABSTRACT Objective The 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. Design We 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. Results Using spearman rank-order correlation, we found the following variables to have significant positive correlation with citations: number of references (r s 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 , R 2 = 0.06) and multi-institutional ( p -value = 0.050 , R 2 = 0.01) had a significant effect on citation rates. Conclusion Editors 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.033 | 0.039 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".