Trichotillomania—psychopathological correlates and associations with health-related quality of life in a large sample
Bibliographic record
Abstract
BACKGROUND: Relatively few studies have assessed the prevalence, correlates, and independent impact on quality of life (QoL) of trichotillomania (TTM) in large samples. METHODS: Consecutive participants (N = 7639) were recruited from a cross-sectional web-based study. Sociodemographic data were collected and several validated self-reported mental health measures were completed (Minnesota Impulsive Disorders Interview, Hypomania checklist, Fagerström Test for Nicotine Dependence, Alcohol Use Disorders Identification Test, Early Trauma Inventory Self Report-Short Form, and the Symptom Checklist-90-Revised Inventory). Health-related QoL was assessed with the World Health Organization QoL abbreviated scale (WHOQOL-Bref). Multivariable models adjusted associations to potential confounders. RESULTS: The sample was predominantly composed of young females (71.3%; mean age: 27.2 ± 7.9 years). The prevalence of probable TTM was 1.4% (95% confidence intervals [CI]: 1.2-1.7), and was more common among females. Participants with probable TTM had a greater likelihood of having co-occurring probable depression (adjusted odds ratio [ORadj] = 1.744; 95% CI: 1.187-2.560), tobacco (ORadj = 2.250; 95% CI: 1.191-4.250), and alcohol (ORadj = 1.751; 95% CI: 1.169-2.621) use disorders. Probable TTM was also independently associated with suicidal ideation (ORadj = 1.917; 95% CI: 1.224-3.003) and exposure to childhood sexual abuse (ORadj = 1.221; 95% CI: 1.098-1.358). In addition, a positive screen for TTM had more impaired physical and mental QoL. CONCLUSIONS: TTM was associated with a positive screen for several psychiatric comorbidities as well as impaired physical and psychological QoL. Efforts towards the recognition and treatment of TTM across psycho-dermatology services are warranted.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".