Recommendations and Guidelines for Creating Scholarly Biomedical Journals: A Scoping Review
Bibliographic record
Abstract
Introduction: The dissemination of new research findings relies heavily on scholarly journals. However, the process of starting new, reputable journals and the challenges encountered in doing so are rarely discussed. This scoping review sought to identify and summarise current suggestions for launching a biomedical journal. Methods: On January 14, 2022, we searched five bibliographic databases, including OVID Medline + Medline in Process, Embase Classic + Embase, ERIC, APA PsycINFO, and Web of Science. On March 19, 2022, a complementary grey literature search was performed. Any English-language source that provided guidelines for creating a biomedical journal in any year and format was eligible. Retrieved sources had their titles and abstracts reviewed. We captured the author's name, publication year and nation, journal name, source type, and any recommendations from the sources that discussed how to launch a biomedical journal. These suggestions were sorted and organized thematically. Results: Overall, 5626 sources were collected. Thirty-three sources satisfied our criterion for inclusion with the majority being blog entries, and only 10 sources provided supporting data. Our analysis of these 33 sources yielded 51 distinct recommendations, which we grouped into nine categories according to their commonalities: journal operations, editorial review procedures, peer review procedures, open access publishing, copyediting/typesetting, production, archiving/indexing/metrics, marketing/promotion, and funding. Discussion/Conclusion: Formal guidelines and recommendations for creating a biomedical journal are currently lacking in the academic community. Forming an evidence-based protocol might support scholarly publishing standards, provide insight into potential challenges for new journals, and offer inexperienced publishers the resources they need to adhere to 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 | 0.399 | 0.645 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.078 | 0.076 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.031 | 0.036 |
| Open science | 0.018 | 0.016 |
| Research integrity | 0.029 | 0.018 |
| Insufficient payload (model declined to judge) | 0.028 | 0.025 |
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".