The reporting of funding in health policy and systems research: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Major research-reporting statements, such as PRISMA and CONSORT, require authors to provide information about funding. The objectives of this study were (1) to assess the reporting of funding in health policy and systems research (HPSR) papers and (2) to assess the funding reporting policies of journals publishing on HPSR. METHODS: We conducted two cross-sectional surveys for papers published in 2016 addressing HPSR (both primary studies and systematic reviews) and for journals publishing on HPSR (both journals under the 'Health Policy and Services' (HPS) category in the Web of Science, and non-HPS journals that published on HPSR). Teams of two reviewers selected studies and abstracted data in duplicate and independently. We conducted descriptive analyses and a regression analysis to investigate the association between reporting of funding by papers and the journal's characteristics. RESULTS: We included 400 studies (200 systematic reviews and 200 primary studies) that were published in 198 journals. Approximately one-third (31%) of HPSR papers did not report on funding. Of those that did, only 11% reported on the role of funders (15% of systematic reviews and 7% of primary studies). Of the 198 journals publishing on HPSR, 89% required reporting of the source of funding. Of those that did, about one-third (34%) required reporting of the role of funders. Journals classified under the HPS category (n = 72) were less likely than non-HPS journals that published HPSR studies (n = 142) to require information on the role of funders (15% vs. 32%). We did not find any of the journals' characteristics to be associated with the reporting of funding by papers. CONCLUSIONS: Despite the majority of journals publishing on HPSR requiring the reporting of funding, approximately one-third of HPSR papers did not report on the funding source. Moreover, few journals publishing on HPSR required the reporting of the role of funders, and few HPSR papers reported on that role.
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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 | 0.214 | 0.433 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".