Nurse editors' views on the peer review process
Bibliographic record
Abstract
A growing body of research challenges the inter-rater reliability of peer reviewers and the value of reviewer training or blinding in improving the quality of manuscript reviews, but double-blinded peer review of papers remains a relatively unexamined standard for nursing journals. Using data from a larger emailed survey, the views of 88 nurse editors on peer review were analyzed using content analysis. The majority of nurse editors reported that blinding was important in peer review, to maintain objectivity and avoid negative personal or professional consequences. The minority who saw potential benefits of open review valued increased transparency in the reviewing and editorial decision-making process. An excellent review was viewed as containing specific instructions on how the deficits in a manuscript might be remedied. Common weaknesses of reviews were lack of specificity and inappropriate focus. Virtually all editors provided some form of preparation or guidance to reviewers. Peer review has an impact on nurses' workload and careers, and training in writing and critique should be included in nursing education.
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 | 0.261 | 0.673 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".