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Record W4385814050 · doi:10.1136/bmjopen-2023-072258

Conflict of interest and funding in health communication on social media: a systematic review

2023· review· en· W4385814050 on OpenAlexaff
Vanessa Helou, Fatima Mouzahem, Adham Makarem, Hussein A Noureldine, Rayane El-Khoury, Dana Al Oweini, Razan Halak, Layal Hneiny, Joanne Khabsa, Elie A. Akl

Bibliographic record

VenueBMJ Open · 2023
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsSocial mediaMedicineConflict of interestChecklistHealth careCitationMEDLINESystematic reviewPaymentFamily medicinePublic relationsLibrary sciencePsychologyWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVES: To synthesise the available evidence on the reporting of conflicts of interest (COI) by individuals posting health messages on social media, and on the reporting of funding sources of studies cited in health messages on social media. DATA SOURCES: MEDLINE (OVID) (2005-March 2022), Embase (2005-March 2022) and Google Scholar (2005-August 2022), supplemented with a review of reference lists and forward citation tracking. DESIGN: Reviewers selected eligible studies and abstracted data in duplicate and independently. We appraised the quality of the included studies using the Mixed Methods Appraisal Tool. We summarised the results in both narrative and tabular formats. We followed the PRISMA 2020 checklist for reporting our study. RESULTS: Of a total of 16 645 retrieved citations, we included 17 eligible studies. The frequency of reporting of conflicts of interest varied between 0% and 60%, but it was mostly low. In addition, a significant proportion, ranging between 15% and 80%, of healthcare professionals using social media have financial relationships with industry. However, three studies assessed the proportion of conflicts of interest of physicians identified through Open Payment Database but not reported by the authors. It was found that 98.7-100% of these relationships with industry are not reported when communicating health-related information. Also, two studies showed that there is evidence of a potential association between COI and the content of posting. No data was found on the reporting of funding sources of studies cited in health messages on social media. CONCLUSIONS: While a significant proportion of healthcare professionals using social media have financial relationships with industry, lack of reporting on COI and undisclosed COI are common. We did not find studies on the reporting of funding sources of studies cited in health messages on social media. TRIAL REGISTRATION: dx.doi.org/10.17504/protocols.io.5jyl8jj4rg2w/v1.

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 imitation

Not 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.

metaresearch head score (Codex)0.085
metaresearch head score (Gemma)0.393
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.915
Threshold uncertainty score0.447

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0850.393
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0090.007
Bibliometrics0.0260.024
Science and technology studies0.0020.003
Scholarly communication0.0080.011
Open science0.0030.004
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.956
GPT teacher head0.741
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designSystematic review
DomainEvaluation
GenreReview

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".

Quick stats

Citations19
Published2023
Admission routes1
Has abstractyes

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