Conflict of interest and funding in health communication on social media: a systematic review v1
Bibliographic record
Abstract
Objective: To synthesize the available evidence on the disclosure of conflicts of interests by individuals posting health messages on social media, and on the reporting of funding sources of studies cited in health messages on social media Study design: systematic review Data Sources: We developed a search strategy, using the help of a librarian, for MEDLINE, EMBASE and Google Scholar electronic databases from 2005 to present. The search combined various keywords and medical subject headings (MeSH) terms relevant to concepts of conflict of interest, funding, and social media. We did not restrict the search to specific languages. Teams of two reviewers will conduct the screening in duplicate and independently. We will also screen the reference lists of included studies as well as other relevant papers. Data abstraction: The reviewers will abstract data from eligible studies in duplicate and independently. We will use a standardized and pilot-tested data abstraction form. We will abstract information on the general characteristics of included studies, characteristics of the social media examined, conflict of interest, and funding. Quality assessment: A team of two reviewers will assess independently the risk of bias of included studies using the relevant part of the Mixed Methods Appraisal Tool.
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 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 | 0.069 | 0.333 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.022 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".