A scoping review protocol on the roles and tasks of peer reviewers in the manuscript review process in biomedical journals
Bibliographic record
Abstract
BACKGROUND: Although peer reviewers play a key role in the manuscript review process, their roles and tasks are poorly defined. Clarity around this issue is important as it may influence the quality of peer reviewer reports. This scoping review explored the roles and tasks of peer reviewers of biomedical journals. METHODS: Comprehensive literature searches were conducted in Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, Educational Resources Information Center, EMBASE, MEDLINE, PsycINFO, Scopus and Web of Science from inception up to May 2017. There were no date and language restrictions. We also searched for grey literature. Studies with statements mentioning roles, tasks and competencies pertaining to the role of peer reviewers in biomedical journals were eligible for inclusion. Two reviewers independently performed study screening and selection. Relevant statements were extracted, collated and classified into themes. RESULTS: After screening 2763 citations and 600 full-text papers, 209 articles and 13 grey literature sources were included. A total of 1426 statements related to roles were extracted, resulting in 76 unique statements. These were grouped into 13 emergent themes: proficient experts in their field (3 items), dutiful/altruistic towards scientific community (7 items), familiar with journal (2 items), unbiased and ethical professionals (18 items), self-critical professionals (4 items), reliable professionals (7 items), skilled critics (15 items), respectful communicators (6 items), gatekeepers (2 items), educators (2 items), advocates for author/editor/reader (3 items) and advisors to editors (2 items). Roles that do not fall within the remit of peer reviewers were also identified (5 items). We also extracted 2026 statements related to peer reviewers' tasks, resulting in 73 unique statements. These were grouped under six themes: organisation and approach to reviewing (10 items), make general comments (10 items), assess and address content for each section of the manuscript (36 items), address ethical aspects (5 items), assess manuscript presentation (8 items) and provide recommendations (4 items). CONCLUSIONS: Peer reviewers are expected to perform a large number of roles and tasks for biomedical journals. These warrant further discussion and clarification in order not to overburden these key actors.
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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 | 0.318 | 0.251 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.052 | 0.022 |
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".