Prevalence of body-focused repetitive behaviors in a diverse population sample – rates across age, gender, race and education
Bibliographic record
Abstract
BACKGROUND: Prevalence estimates for body-focused repetitive behaviors (BFRBs) such as trichotillomania differ greatly across studies owing to several confounding factors (e.g. different criteria). For the present study, we recruited a diverse online sample to provide estimates for nine subtypes of BFRBs and body-focused repetitive disorders (BFRDs). METHODS: The final sample comprised 1481 individuals from the general population. Several precautions were taken to recruit a diverse sample and to exclude participants with low reliability. We matched participants on gender, race, education and age range to allow unbiased interpretation. RESULTS: While almost all participants acknowledged at least one BFRB in their lifetime (97.1%), the rate for BFRDs was 24%. Nail biting (11.4%), dermatophagia (8.7%), skin picking (8.2%), and lip-cheek biting (7.9%) were the most frequent BFRDs. Whereas men showed more lifetime BFRBs, the rate of BFRDs was higher in women than in men. Rates of BFRDs were low in older participants, especially after the age of 40. Overall, BFRBs and BFRDs were more prevalent in White than in non-White individuals. Education did not show a strong association with BFRB/BFRDs. DISCUSSION: BFRBs are ubiquitous. More severe forms, BFRDs, manifest in approximately one out of four people. In view of the often-irreversible somatic sequelae (e.g. scars) BFRBs/BFRDs deserve greater diagnostic and therapeutic attention by clinicians working in both psychology/psychiatry and somatic medicine (especially dermatology and dentistry).
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".