Addressing misleading medical information on social media: a scoping review of current interventions
Bibliographic record
Abstract
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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Scholarly communication Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.011 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".