Recommendations and guidelines for creating scholarly biomedical journals: A scoping review
Bibliographic record
Abstract
BACKGROUND: Scholarly journals play a key role in the dissemination of research findings. However, little focus is given to the process of establishing new, credible journals and the obstacles faced in achieving this. This scoping review aimed to identify and describe existing recommendations for starting a biomedical scholarly journal. METHODS: We searched five bibliographic databases: OVID Medline + Medline in Process, Embase Classic + Embase, ERIC, APA PsycINFO, and Web of Science on January 14, 2022. A related grey literature search was conducted on March 19, 2022. Eligible sources were those published in English in any year, of any format, and that described guidance for starting a biomedical journal. Titles and abstracts of obtained sources were screened. We extracted descriptive characteristics including author name, year and country of publication, journal name, and source type, and any recommendations from the included sources discussing guidance for starting a biomedical journal. These recommendations were categorized and thematically grouped. RESULTS: A total of 5626 unique sources were obtained. Thirty-three sources met our inclusion criteria. Most sources were blog posts (10/33; 30.30%), and only 10 sources were supported by evidence. We extracted 51 unique recommendations from these 33 sources, which we thematically classified into nine themes which were: journal operations, editorial review processes, peer review processes, open access publishing, copyediting/typesetting, production, archiving/indexing/metrics, marketing/promotion, and funding. CONCLUSIONS: There is little formal guidance regarding how to start a scholarly journal. The development of an evidence-based guideline may help uphold scholarly publishing quality, provide insight into obstacles new journals will face, and equip novice publishers with the tools to meet best practices.
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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 | 0.412 | 0.667 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.069 | 0.053 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.027 | 0.035 |
| Open science | 0.016 | 0.016 |
| Research integrity | 0.021 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".