Viral voices: Depictions of women’s pain experiences on social media
Bibliographic record
Abstract
TikTok is a popular social media platform increasingly used to disseminate health information and personal experiences, including among women with pain. Characterizing health-related content can help understand how public perceptions are shaped and guide improvements in patient care. Although women with pain often seek information on social media, little is known about social media content pertaining to women's pain. In this study, the content, characteristics, and engagement metrics of the top 100 TikTok videos on women's pain were analyzed. "Women's pain" was searched on TikTok using TikTok's proprietary algorithm. A total of 140 videos were retained for preliminary extraction, and the first 100 that met inclusion criteria were analyzed. Qualitative content analysis of video content was performed, leading to the development of 15 content categories. Of these categories, 66.6% (10/15) represented aspects of women's pain experiences characterized as having a negative tone, including "visual depiction of being in pain," "minimizing/dismissing/gaslighting women's pain," "ineffective pain treatment," "women's pain not being investigated enough," and "assuming women's pain is due to menstruation, motherhood, or mental health issues." Descriptive analyses indicated that the top 100 videos had a combined 338.8 million views and 35.1 million likes. Most videos featured non-healthcare providers' creators (76.0%). Across content categories, the highest engagement rates were observed for the category "women's pain is not understood by others" (15.0%). Overall, strong negative trends were observed in TikTok video content pertaining to women's pain. These findings underscore the urgent need for improved pain care for women experiencing pain. PERSPECTIVE: This article reports on the content, characteristics, and engagement metrics of the top 100 TikTok videos pertaining to women's pain. These findings provide clinicians and researchers with important insights into women's pain experiences and have the potential to inform future research, education, and training initiatives aimed at improving women's pain management.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".