Conflicts of interest policies for authors, peer reviewers, and editors of bioethics journals
Bibliographic record
Abstract
BACKGROUND: In biomedical research, there have been numerous scandals highlighting conflicts of interest (COIs) leading to significant bias in judgment and questionable practices. Academic institutions, journals, and funding agencies have developed and enforced policies to mitigate issues related to COI, especially surrounding financial interests. After a case of editorial COI in a prominent bioethics journal, there is concern that the same level of oversight regarding COIs in the biomedical sciences may not apply to the field of bioethics. In this study, we examined the availability and comprehensiveness of COI policies for authors, peer reviewers, and editors of bioethics journals. METHODS: After developing a codebook, we analyzed the content of online COI policies of 63 bioethics journals, along with policy information provided by journal editors that was not publicly available. RESULTS: Just over half of the bioethics journals had COI policies for authors (57%), and only 25% for peer reviewers and 19% for editors. There was significant variation among policies regarding definitions, the types of COIs described, the management mechanisms, and the consequences for noncompliance. Definitions and descriptions centered on financial COIs, followed by personal and professional relationships. Almost all COI policies required disclosure of interests for authors as the primary management mechanism. Very few journals outlined consequences for noncompliance with COI policies or provided additional resources. CONCLUSION: Compared to other studies of biomedical journals, a much lower percentage of bioethics journals have COI policies and these vary substantially in content. The bioethics publishing community needs to develop robust policies for authors, peer reviewers, and editors and these should be made publicly available to enhance academic and public trust in bioethics scholarship.
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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 | 0.243 | 0.664 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".