Requirements of health policy and services journals for authors to disclose financial and non-financial conflicts of interest: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The requirements of the health policy and services journals for authors to report their financial and non-financial conflicts of interest (COI) are unclear. The present article aims to assess the requirements of health policy and services journals for authors to disclose their financial and non-financial COIs. METHODS: This is a cross-sectional study of journals listed by the Web of Science under the category of 'Health Policy and Services'. We reviewed the 'Instructions for Authors' on the journals' websites and then simulated the submission of a manuscript to obtain any additional relevant information made available during that step. We abstracted data in duplicate and independently using a standardised form. RESULTS: Out of 72 eligible journals, 67 (93%) had a COI policy. A minority of policies described how the disclosed COIs of authors would impact the editorial process (34%). None of the policies had clear-cut criteria for rejection based on the content of the disclosure. Approximately a fifth of policies (21%) explicitly stated that inaccurate or incomplete disclosures might lead to manuscript rejection or retraction. No policy described whether the journal would verify the accuracy or completeness of authors' disclosed COIs. Most journals' policies (93%) required the disclosure of at least one form of financial COI. While the majority asked for specification of source of payment (71%), a minority asked for the amount (18%). Overall, 81% of policies explicitly required disclosure of non-financial COIs. CONCLUSION: A majority of health policy and services journal policies required the disclosure of authors' financial and non-financial COIs, but few required details on disclosed COIs. Health policy journals should provide specific definitions and instructions for disclosing non-financial COIs. A framework providing clear typology and operational definitions of the different types of COIs will facilitate both their disclosure by authors and reviewers and their assessment and management by the editorial team and the readers.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchResearch integrity Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.044 | 0.176 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".