Social Media Marketing of Non-Evidence-Based Women's Health Interventions: Protocol for a Content Analysis Using Participatory Research Methods
Bibliographic record
Abstract
BACKGROUND: The promotion of non-evidence-based health interventions to women on social media is a growing problem. OBJECTIVE: This study aims to explore the use of social media to disseminate and promote health interventions that lack robust evidence and are of current interest and popularity. METHODS: A content analysis of posts on TikTok, Instagram, and Facebook about 5 health interventions targeted at women will be conducted using participatory research methods with consumers. English-language posts that discuss boric acid suppositories, fertility testing, perimenopause and menopause testing, supplements and hormone treatments for menopause, and menopause hormone therapy for disease prevention will be included. Using keyword searches related to each health intervention, consumers will screen the top posts until 100 eligible posts on 2 different social media platforms are identified (1000 posts total across the 5 health interventions). Data from the post's caption, on-screen text, and audio and/or video will be included in the analysis. The analysis of these posts will take both a deductive approach using a prespecified framework and an inductive approach, generating key themes from the post content. RESULTS: Data on TikTok, Instagram, and Facebook have been searched and screened. Development of the coding framework and analysis is now underway. The findings will be disseminated via publications in peer-reviewed international medical journals and presentations at national and international conferences in 2025 and 2026. CONCLUSIONS: This novel study will provide important insights into how information on various women's health interventions and products, which currently lack robust evidence of benefit, are being disseminated and promoted on social media to women. Understanding this is essential for developing strategies to mitigate potential harm and plan solutions, thus protecting women from the low-value interventions marketed to them, becoming patients unnecessarily, and taking finite resources away from the health care system. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/76750.
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 | 0.090 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.011 |
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".