Reporting of conflicts of interest by authors of primary studies on health policy and systems research: a cross-sectional survey
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to assess the frequency and types of conflict of interest (COI) disclosed by authors of primary studies of health policy and systems research (HPSR). DESIGN: We conducted a cross-sectional survey using standard systematic review methodology for study selection and data extraction. We conducted descriptive analyses. SETTING: We collected data from papers published in 2016 in 'health policy and service journals' category in Web of Science database. PARTICIPANTS: We included primary studies (eg, randomised controlled trials, cohort studies, qualitative studies) of HPSR published in English in 2016 peer-reviewed health policy and services journals. OUTCOME MEASURES: Reported COI disclosures including whether authors reported COI or not, form in which COI disclosures were provided, number of authors per paper who report any type of COI, number of authors per paper who report specific types and subtypes of COI. RESULTS: We included 200 eligible primary studies of which 132 (66%) included COI disclosure statements of authors. Of the 132 studies, 19 (14%) had at least one author reporting at least one type of COI and the most frequently reported type was individual financial COI (n=15, 11%). None of the authors reported individual intellectual COIs or personal COIs. Financial and individual COIs were reported more frequently compared with non-financial and institutional COIs. CONCLUSION: A low percentage of HPSR primary studies included authors reporting COI. Non-financial or institutional COIs were the least reported types of 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 | 0.245 | 0.462 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| 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; 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".