Journal editors’ perspectives on the communication practices in biomedical journals: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: To generate an understanding of the communication practices that might influence the peer-review process in biomedical journals. METHOD: Recruitment was based on purposive maximum variation sampling. We conducted semistructured interviews. Data were analysed using thematic analysis method. PARTICIPANTS: 56 journal editors from general medicine (n=13) and specialty (n=43) biomedical journals. Most were editor-in-chiefs (n=39), men (n=40) and worked part time (n=50). RESULTS: Our analysis generated four themes (1) providing minimal guidance to peer reviewers-two subthemes described the way journal editors rationalised their behaviour: (a) peer reviewers should know without guidelines how to review and (b) detailed guidance and structure might have a negative effect; (2) communication strategies of engagement with peer reviewers-two opposing strategies that journal editors employed to handle peer reviewers: (a) use of direct and personal communication to motivate peer reviewers and (b) use of indirect communication to avoid conflict; (3) concerns about impact of review model on communication-maintenance of anonymity as a means of facilitating critical and unburdened communication and minimising biases and (4) different practices in the moderation of communication between authors and peer reviewers-some journal editors actively interjected themselves into the communication chain to guide authors through peer reviewers' comments, others remained at a distance, leaving it to the authors to work through peer reviewers' comments. CONCLUSIONS: These journal editors' descriptions reveal several communication practices that might have a significant impact on the peer-review process. Editorial strategies to manage miscommunication are discussed. Further research on these proposed strategies and on communication practices from the point of view of authors and peer reviewers is warranted.
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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 | 0.038 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".