The Portrayal of Tourette Syndrome in Film and Television
Bibliographic record
Abstract
OBJECTIVE: To determine the representation of Tourette Syndrome (TS) in fictional movies and television programs by investigating recurrent themes and depictions. BACKGROUND: Television and film can be a source of information and misinformation about medical disorders. Tourette Syndrome has received attention in the popular media, but no studies have been done on the accuracy of the depiction of the disorder. METHODS: International internet movie databases were searched using the terms "Tourette's", "Tourette's Syndrome", and "tics" to generate all movies, shorts, and television programs featuring a character or scene with TS or a person imitating TS. Using a grounded theory approach, we identified the types of characters, tics, and co-morbidities depicted as well as the overall representation of TS. RESULTS: Thirty-seven television programs and films were reviewed dating from 1976 to 2010. Fictional movies and television shows gave overall misrepresentations of TS. Coprolalia was overrepresented as a tic manifestation, characters were depicted having autism spectrum disorder symptoms rather than TS, and physicians were portrayed as unsympathetic and only focusing on medical therapies. School and family relationships were frequently depicted as being negatively impacted by TS, leading to poor quality of life. CONCLUSIONS: Film and television are easily accessible resources for patients and the public that may influence their beliefs about TS. Physicians should be aware that TS is often inaccurately represented in television programs and film and acknowledge misrepresentations in order to counsel patients accordingly.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".