Reporting of Financial and Non-financial Conflicts of Interest in Systematic Reviews on Health Policy and Systems Research: A Cross Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Systematic reviews are increasingly used to inform health policy-making. The conflicts of interest (COI) of the authors of systematic reviews may bias their results and influence their conclusions. This may in turn lead to misguided public policies and systems level decisions. In order to mitigate the adverse impact of COI, scientific journals require authors to disclose their COIs. The objective of this study was to assess the frequency and different types of COI that authors of systematic reviews on health policy and systems research (HSPR) report. METHODS: We conducted a cross sectional survey. We searched the Health Systems Evidence (HSE) database of McMaster Health Forum for systematic reviews published in 2015. We extracted information regarding the characteristics of the systematic reviews and the associated COI disclosures. We conducted descriptive analyses. RESULTS: Eighty percent of systematic reviews included authors' COI disclosures. Of the 160 systematic reviews that included COI disclosures, 15% had at least one author reporting at least one type of COI. The two most frequently reported types of COI were individual financial COI and individual scholarly COI (11% and 4% respectively). Institutional COIs were less commonly reported than individual COIs (3% and 15% respectively) and non-financial COIs were less commonly reported than financial COIs (6% and 14% respectively). Only one systematic review reported the COI disclosure by editors, and none reported disclosure by peer reviewers. All COI disclosures were in the form of a narrative statement in the main document and none in an online document. CONCLUSION: A fifth of systematic reviews in HPSR do not include a COI disclosure statement, highlighting the need for journals to strengthen and/or better implement their COI disclosure policies. While only 15% of identified disclosure statements report any COI, it is not clear whether this indicates a low frequency of COI versus an underreporting of COI, or both.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".