Conflict of interest policies for editors and peer reviewers in medical journals: cross-sectional study
Bibliographic record
Abstract
Objectives: Editors and reviewers of research manuscripts may have conflicts of interest that impact their evaluations. We aimed to characterise medical journals' conflict of interest policies for editors and peer reviewers.Study design and setting: In this cross-sectional study, we randomly sampled 277 medical journals from Clarivate Journal Citation Reports. Two authors independently retrieved public conflict of interest policies and disclosures for editors and peer reviewers from journal websites, and retrieved publishers’ policies when journals also referred to them (January to June 2024). We used content analysis to analyse policies and multivariable mixed-effects logistic regressions to estimate the associations between journal characteristics and having a policy. Results: After excluding 27 journals, we included 250 medical journals in English, of which 177 (71%) had a conflict of interest policy for editors and 174 (70%) for peer reviewers. Of journals with a policy, 137 (77%) and 129 (74%) described disclosure requirements, 160 (90%) and 163 (94%) management strategies, 124 (70%) and 106 (61%) policy enforcement strategies, and 17 (10%) and 15 (9%) processes for appealing decisions. All four concepts were addressed in 16 (9%) policies for editors and 11 (6%) for peer reviewers. Having a policy for editors was associated with higher journal impact factor (adjusted odds ratio (OR): 1.28; 95% confidence interval (CI): 1.05–1.56) and Committee on Publication Ethics (COPE) membership (OR: 3.50; 95% CI: 1.42–8.65). Having a policy for peer reviewers was associated with higher journal impact factor (OR: 1.16; 95% CI: 0.97–1.37) and open access journal (OR: 4.59; 95% CI: 1.11–18.93). For a subgroup of journals referring to their publishers’ policy, the content was concordant for 5 (11%) of 45 journals for editors and 4 (9%) of 47 journals for peer reviewers. Of 250 journals, 14 (6%) had public declarations of interest from editors, and 3 (1%) from peer reviewers.Conclusion: More than two-thirds of medical journals have conflict of interest policies for editors and reviewers; however, policies vary in comprehensiveness, and content is rarely concordant with publishers’ policies. There is potential to improve the content of conflict of interest policies and the transparency of interests in medical journals.
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 | 0.022 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".