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Record W4414833709 · doi:10.1136/bmjebm-2025-113704

Addressing misleading medical information on social media: a scoping review of current interventions

2025· review· en· W4414833709 on OpenAlexaff
Emma Grundtvig Gram, Ray Moynihan, Tessa Copp, Patti Shih, Loai Albarqouni, Elie A. Akl, Courtney Smith, Leah Hardiman, Brooke Nickel

Bibliographic record

VenueBMJ evidence-based medicine · 2025
Typereview
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsMcMaster UniversityImpact
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsPsychological interventionSocial mediaPublic healthPublic policyMedical informationMEDLINEDigital health

Abstract

fetched live from OpenAlex

BACKGROUND: Misleading information about medical products on social media may cause overuse. OBJECTIVES: Explore interventions targeting the problem of misleading medical information and marketing on social media, with a focus on preventing medical overuse including overdiagnosis. ELIGIBILITY CRITERIA: We included peer-reviewed studies with original data on an intervention targeting misleading medical information on social media and governmental/institutional responses with and without evaluation. We excluded responses relating to COVID-19. SOURCES OF EVIDENCE: four electronic databases: MEDLINE/PubMed, PsycINFO, Academic Search Complete and Web of Science, and searches of grey literature on Google and Google Scholar. Search date: 9 June 2025. DATA CHARTING: We used prespecified data forms populated in duplicate by two reviewers. RESULTS: We identified 27 peer-reviewed articles and 25 organisational and governmental responses (grey literature). 20 (74%) of the peer-reviewed interventions targeted the consumer to enhance 'media literacy', support decision-making or warn about misinformation trends. Approaches included education, such as videos or information materials, to improve detection of misinformation, as well as correcting misinformation and rebutting claims. Only two (7.4%) of the peer-reviewed approaches were sensitive to the problem of medical overuse: a risk-of-deception tool and an informed decision-making service. The grey literature about government and organisational responses chiefly comprised general advertising regulations and other educational resources for consumers to identify and navigate misinformation. The advertising regulations ranged from self-regulatory codes of practice to mandatory regulations, requiring pre-approval of social media marketing material. Most regulations stated advertising should be truthful, presenting both benefits and harms and not be misleading. Most of the grey literature (64%) was sensitive to medical overuse, though none referred explicitly to the problem. CONCLUSIONS: Current efforts to address misleading medical marketing on social media often overlook the critical issue of medical overuse and fail to provide sufficient consumer protections in this rapidly evolving digital landscape of social media, such as the speed of dissemination, reach and the role of third-party advertising. These gaps in research, regulation and practice present significant opportunities to strengthen evidence-based policies and public health responses. TRIAL REGISTRATION DETAILS: https://doi.org/10.17605/OSF.IO/2NJSH.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScholarly communication
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewmedium
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.120
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.574
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.120
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.633
GPT teacher head0.615
Teacher spread0.018 · 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

Labeled directly by 2 models reading the full record.

Scholarly communication

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designSystematic review
Domainnot available
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

Citations6
Published2025
Admission routes1
Has abstractyes

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