TikTok and Attention-Deficit/Hyperactivity Disorder: A Cross-Sectional Study of Social Media Content Quality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objectives Social media platforms are increasingly being used to disseminate mental health information online. User-generated content about attention-deficit/hyperactivity disorder (ADHD) is one of the most popular health topics on the video-sharing social media platform TikTok. We sought to investigate the quality of TikTok videos about ADHD. Method The top 100 most popular videos about ADHD uploaded by TikTok video creators were classified as misleading, useful, or personal experience. Descriptive and quantitative characteristics of the videos were obtained. The Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V) and Journal of American Medical Association (JAMA) benchmark criteria were used to assess the overall quality, understandability, and actionability of the videos. Results Of the 100 videos meeting inclusion criteria, 52% ( n = 52) were classified as misleading, 27% ( n = 27) as personal experience, and 21% ( n = 21) as useful. Classification agreement between clinician ratings was 86% (kappa statistic of 0.7766). Videos on the platform were highly understandable by viewers but had low actionability. Non-healthcare providers uploaded the majority of misleading videos. Healthcare providers uploaded higher quality and more useful videos, compared to non-healthcare providers. Conclusions Approximately half of the analyzed TikTok videos about ADHD were misleading. Clinicians should be aware of the widespread dissemination of health misinformation on social media platforms and its potential impact on clinical care.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it