Public Perception of Tourette Syndrome on YouTube
Bibliographic record
Abstract
We sought to determine public perception surrounding Tourette syndrome through viewers' responses to videos on YouTube. The top 20 videos on YouTube for search terms Tourette's, Tourette's syndrome, Tourette syndrome and tics were selected. The portrayal of Tourette syndrome was assessed as positive, negative, or neutral. Top 10 comments for each video were graded as "sympathetic," "neutral," or "derogatory." A total of 14 970 hits were obtained and 41 videos were retained, with an average of 590 113 views (1369 to 13 747 069) and 1761 comments (0 to 35 241). Twenty-two percent of videos retained portrayed Tourette syndrome negatively, 20% were neutral and 59% positive. Negative portrayals were significantly associated with more views (Spearman correlation rho = -.46, P =.003) and comments (Spearman correlation rho = -.47, P = .002). Although excellent examples of Tourette syndrome are available on YouTube, the popularity of negative portrayals may reinforce existing stigma in society.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".