Systematic review of the effectiveness of training programs in writing for scholarly publication, journal editing, and manuscript peer review (protocol)
Bibliographic record
Abstract
BACKGROUND: An estimated $100 billion is lost to 'waste' in biomedical research globally, annually, much of which comes from the poor quality of published research. One area of waste involves bias in reporting research, which compromises the usability of published reports. In response, there has been an upsurge in interest and research in the scientific process of writing, editing, peer reviewing, and publishing (that is, journalology) of biomedical research. One reason for bias in reporting and the problem of unusable reports could be due to authors lacking knowledge or engaging in questionable practices while designing, conducting, or reporting their research. Another might be that the peer review process for journal publication has serious flaws, including possibly being ineffective, and having poorly trained and poorly motivated reviewers. Similarly, many journal editors have limited knowledge related to publication ethics. This can ultimately have a negative impact on the healthcare system. There have been repeated calls for better, more numerous training opportunities in writing for publication, peer review, and publishing. However, little research has taken stock of journalology training opportunities or evaluations of their effectiveness. METHODS: We will conduct a systematic review to synthesize studies that evaluate the effectiveness of training programs in journalology. A comprehensive three-phase search approach will be employed to identify evaluations of training opportunities, involving: 1) forward-searching using the Scopus citation database, 2) a search of the MEDLINE In-Process and Non-Indexed Citations, MEDLINE, Embase, ERIC, and PsycINFO databases, as well as the databases of the Cochrane Library, and 3) a grey literature search. DISCUSSION: This project aims to provide evidence to help guide the journalological training of authors, peer reviewers, and editors. While there is ample evidence that many members of these groups are not getting the necessary training needed to excel at their respective journalology-related tasks, little is known about the characteristics of existing training opportunities, including their effectiveness. The proposed systematic review will provide evidence regarding the effectiveness of training, therefore giving potential trainees, course designers, and decision-makers evidence to help inform their choices and policies regarding the merits of specific training opportunities or types of training.
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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 | 0.154 | 0.332 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".