A double-edged hashtag: Evaluation of #ADHD-related TikTok content and its associations with perceptions of ADHD
Bibliographic record
Abstract
We aimed to assess the psychoeducational quality of TikTok content about attention-deficit/hyperactivity disorder (ADHD) from the perspective of both mental health professionals and young adults across two pre-registered studies. In Study 1, two clinical psychologists with expertise in ADHD evaluated the claims (accuracy, nuance, overall quality as psychoeducation material) made in the top 100 #ADHD TikTok videos. Despite the videos' immense popularity (collectively amassing nearly half a billion views), fewer than 50% of the claims about ADHD symptoms were judged to align with the Diagnostic and Statistical Manual of Mental Disorders. In Study 2, 843 undergraduate students (no ADHD = 224, ADHD self-diagnosis = 421, ADHD formal diagnosis = 198) were asked about their typical frequency of viewing #ADHD content on TikTok and their perceptions of ADHD and were shown the top 5 and bottom 5 psychologist-rated videos from Study 1. A greater typical frequency of watching ADHD-related TikToks was linked to a greater willingness to recommend both the top and bottom-rated videos from Study 1, after controlling for demographics and ADHD diagnostic status. It was also linked to estimating a higher prevalence of ADHD in the general population and greater challenges faced by those with ADHD. Our findings highlight a discrepancy between mental health professionals and young adults regarding the psychoeducational value of #ADHD content on TikTok. Addressing this is crucial to improving access to treatment and enhancing support for those with ADHD.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".