A systematic review highlights a knowledge gap regarding the effectiveness of health-related training programs in journalology
Bibliographic record
Abstract
OBJECTIVES: To investigate whether training in writing for scholarly publication, journal editing, or manuscript peer review effectively improves educational outcomes related to the quality of health research reporting. STUDY DESIGN AND SETTING: We searched MEDLINE, Embase, ERIC, PsycINFO, and the Cochrane Library for comparative studies of formalized, a priori-developed training programs in writing for scholarly publication, journal editing, or manuscript peer review. Comparators included the following: (1) before and after administration of a training program, (2) between two or more training programs, or (3) between a training program and any other (or no) intervention(s). Outcomes included any measure of effectiveness of training. RESULTS: Eighteen reports of 17 studies were included. Twelve studies focused on writing for publication, five on peer review, and none fit our criteria for journal editing. CONCLUSION: Included studies were generally small and inconclusive regarding the effects of training of authors, peer reviewers, and editors on educational outcomes related to improving the quality of health research. Studies were also of questionable validity and susceptible to misinterpretation because of their risk of bias. This review highlights the gaps in our knowledge of how to enhance and ensure the scientific quality of research output for authors, peer reviewers, and journal editors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".