Trichotillomania: the impact of treatment history on the outcome of an Internet-based intervention
Bibliographic record
Abstract
Steffi Weidt,1 Annette Beatrix Bruehl,2,3 Aba Delsignore,1 Gwyneth Zai,2,4–6 Alexa Kuenburg,1 Richard Klaghofer,1 Michael Rufer1 1Department of Psychiatry and Psychotherapy, University Hospital Zurich, University of Zurich, Zurich, Switzerland; 2Department of Psychiatry, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK; 3Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry, Zurich, Switzerland; 4Department of Psychiatry, Institute of Medical Science, University of Toronto, 5Neurogenetics Section, Centre for Addiction and Mental Health, 6Department of Psychiatry, Frederick W. Thompson Anxiety Disorders Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada Background: Many patients suffering from trichotillomania (TTM) have never undergone treatment. Without treatment, TTM often presents with a chronic course. Characteristics of TTM individuals who have never been treated (untreated) remain largely unknown. Whether treatment history impacts Internet-based interventions has not yet been investigated. We aimed to answer whether Internet-based interventions can reach untreated individuals and whether treatment history is associated with certain characteristics and impacts on the outcome of an Internet-based intervention.Methods: We provided Internet-based interventions. Subjects were characterized at three time points using the Massachusetts General Hospital Hairpulling Scale, Hamilton Depression Rating Scale, and the World Health Organization Quality of Life questionnaire.Results: Of 105 individuals, 34 were untreated. Health-related quality of life (HRQoL) was markedly impaired in untreated and treated individuals. Symptom severity did not differ between untreated and treated individuals. Nontreatment was associated with fewer depressive symptoms (P=0.002). Treatment history demonstrated no impact on the outcome of Internet-based interventions.Conclusion: Results demonstrate that Internet-based interventions can reach untreated TTM individuals. They show that untreated individuals benefit as much as treated individuals from such interventions. Future Internet-based interventions should focus on how to best reach/support untreated individuals with TTM. Additionally, future studies may examine whether Internet-based interventions can reach and help untreated individuals suffering from other psychiatric disorders. Keywords: trichotillomania, health-related quality of life, treatment experience, Internet, online, hairpulling
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 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".