Public health journals’ requirements for authors to disclose funding and conflicts of interest: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Public health journals need to have clear policies for reporting the funding of studies and authors' personal financial and non-financial conflicts of interest (COI) disclosures. This study aims to assess the policies of public health journals on reporting of study funding and the disclosure of authors' COIs. METHODS: This is a cross-sectional study of "Public, Environmental & Occupational Health" journals. Teams of two researchers abstracted data in duplicate and independently using REDCap software. RESULTS: Of 173 public health journals, 155 (90%) had a policy for reporting study funding information. Out of these, a majority did not require reporting of the phase of the study for which funding was received (88%), nor the types of funding sources (87%). Of the 173 journals, 163 (94%) had a policy requiring disclosure of authors' COI. However, the majority of these journals did not require financial conflicts of interest disclosures relating to institutions (75%) nor to the author's family members (90%) while 56% required the disclosure of at least one form of non-financial COI. CONCLUSIONS: The policies of the majority of public health journals do not require the reporting of important details such as the role of the funder, and non-financial COI. Journals and publishers should consider revising their editorial policies to ensure complete and transparent reporting of funding and COI.
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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 | 0.078 | 0.293 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".