Accessibility and transparency of editor conflicts of interest policy instruments in medical journals
Bibliographic record
Abstract
BACKGROUND: There has been significant discussion about the need to manage conflict of interest (COI) in medical journals. This has lead many journals to implement policies to manage COI for authors and reviewers; however, surprisingly little attention has been focused on the COI of journal editors. OBJECTIVE: The goal of this exploratory study was to determine whether the policies were accessible to the public and to researchers, and to discuss the potential impact on public transparency. DESIGN: The authors conducted an internet search of editor COI policy instruments that have been developed, implemented and communicated by the top 10 peer-reviewed medical journals (2010 ISI Web of Knowledge Impact Factor), and assessed their general accessibility by gauging the level of difficulty in navigating the journal's website (number of clicks to find the policy instruments). RESULTS: Only four of the 10 medical journals (40%) in this study have accessible COI policy directives that include editors (JIM, PLoS Medicine, AIM, CMAJ). One journal (NEJM) had an editorial on the subject, and another (The Lancet) mentioned editor COI in their general guidelines. These documents are not readily accessible; starting from the journal's main website at least four clicks are needed to access these documents. CONCLUSION: These results suggest that there is a general lack of accessible editor COI policy instruments among leading medical journals, something that may consequently have a negative impact on the trust accorded to these journals.
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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 | 0.179 | 0.685 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".