Translating Editor COI Values to Action: The Missing Link
Bibliographic record
Abstract
Introduction: Conflict of interest (COI) exists when an individual in the publication process has a competing interest that could compromise their publication process responsibility. COI is commonly associated with authors and less so with editors. Many organizations (e.g. World Association of Medical Editors (WAME)) provide resources and recommendations for addressing COIs at medical journals. However, there are no data describing journals’ utilization of these resources for editor COI policy development or adoption, and little data on the value of editor COI policies. This study aimed to understand current editor COI practices and editors’ perceptions of COI policies, along with barriers to their implementation. Methods: An online survey developed in LimeSurveyTM was distributed to editorial board members of oncology and health care sciences and services journals to measure respondents’ attitudes about COI definitions and features and COI policy experience; barriers to implementing editor COI policies; and editors’ perceptions of COI policies. Frequency analysis of survey data was conducted. Free-text responses were summarized. Results: Response rate was 20.2% (66/327), and comprised complete and partial survey respondents. The majority of respondents were editors-in-chief. Overall, respondents agreed that defined WAME COI domains were important components of an editor COI policy. Nearly 50% of respondents belonged to journals with existing editor COI policies, which they continued to use. Nearly 25% were unaware of the current editor COI policy status at their journal. Few implementation barriers were identified, the most common being challenges with verification of disclosures. Overall, respondents did not report strong attitudes in favour of or against editor COI policies, but respondents agreed that journals with an editor COI policy were more credible and trustworthy. Conclusion: This study shows that editor COI policy development and utilization is not a universal standard of practice and suggests that recognition of the value of an editor COI policy may not be widespread among editorial board members.
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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 | 0.077 | 0.433 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".