Clarivate listed nursing journals in 2020: what they publish and how they measure use of social media
Bibliographic record
Abstract
Abstract Objectives To investigate what the most common types of articles that nursing journals purport to publish are and what they actually publish. And to investigate the extent to which academic nursing journals listed by Clarivate track alternative metrics. Methods Journals included in the nursing Journal Citation Report (JCR) journal category in 2019 described as nursing were identified and considered suitable for inclusion in the analysis. Instructions for authors were reviewed online and mention of each type of article is identified. The tables of contents of each issue of each journal published during 2019 were examined and the types of articles published were extracted to a spreadsheet into permitted article types and published articles. Likewise, the use of alternative metrics by each journal was extracted to a spreadsheet. Pearson's and Spearman's correlation analysis was applied to investigate the relationship between articles permitted and articles published. Results In the 2020 JCR, 123 journals were listed. The most common article type permitted was original research ( n = 117), followed by review papers ( n = 116), and discussion papers ( n = 63). Original research ( n = 7045); review papers ( n = 1268); discussion papers ( n = 1225); editorials ( n = 793) and commentaries ( n = 776) were the most commonly published categories of the article. Of journals examined, 108 (96.8%) tracked mentions on social media and the Altmetric score was most commonly used (75%). There was a strong correlation ( r = 0.73; P = 0.002) between the numbers of articles permitted and published and a strong correlation (ρ = 0.86; P < 0.001) in terms of the rankings of the permitted and published articles. Conclusions There is a relationship between the most frequently permitted article types and those published, especially for the most frequent categories of both. Original articles, review papers, and discussion papers are the backbone of academic publishing in nursing with original articles vastly outweighing review and discussion papers. Most Clarivate listed journals now use some method of tracking alternative metrics indicating how seriously publishers take their social media profiles.
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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 | 0.018 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.050 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".