Skin picking disorder: prevalence, correlates, and associations with quality of life in a large sample
Bibliographic record
Abstract
OBJECTIVE: Evidence suggests that skin picking disorder (SPD) could be a prevalent condition associated with comorbidity and psychosocial dysfunction. However, just a few studies have assessed the prevalence and correlates of SPD in samples from low- and middle-income countries. In addition, the impact of SPD on quality of life (QoL) dimension after multivariable adjustment to potential confounders remains unclear. METHODS: Data were obtained from a Brazilian anonymous Web-based research platform. Participants provided sociodemographic data and completed the modified Skin Picking-Stanford questionnaire, the Hypomania Checklist (HCL-32), the Patient Health Questionnaire-9 (PHQ-9), the Fagerström Test for Nicotine Dependence, Alcohol Use Disorder Identification Test (AUDIT), Symptom Checklist-90-Revised inventory (SCL-90R), early trauma inventory self report-short form, and the World Health Organization quality of life abbreviated scale (WHOQOL-Bref). Associations were adjusted to potential confounders through multivariable models. RESULTS: For our survey, 7639 participants took part (71.3% females; age: 27.2±7.9 years). The prevalence of SPD was 3.4% (95% CI: 3.0-3.8%), with a female preponderance (P<0.001). In addition, SPD was associated with a positive screen for a major depressive episode, nicotine dependence, and alcohol dependence, as well as suicidal ideation. Physical and psychological QoL was significantly more impaired in participants with SPD compared to those without SPD, even after adjustment for comorbidity. CONCLUSIONS: In this large sample, SPD was a prevalent condition associated with co-occurring depression, nicotine, and alcohol dependence. In addition, SPD was independently associated with impaired physical and psychological QoL. Public health efforts toward the early recognition and treatment of SPD 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".