Childhood maltreatment linked to greater symptom severity and poorer quality of life and function in social anxiety disorder
Bibliographic record
Abstract
BACKGROUND: There is a paucity of data examining the prevalence and impact of childhood maltreatment in patients presenting with a primary diagnosis of social anxiety disorder (SAD). We thus examined the presence of a broad spectrum of childhood maltreatment, including physical, sexual, and emotional abuse and neglect, in treatment-seeking individuals with the generalized subtype of SAD (GSAD). We hypothesized that a history of childhood maltreatment would be associated with greater SAD symptom severity and poorer associated function. METHODS: One hundred and three participants with a primary diagnosis of GSAD (mean age 37+/-14; 70% male) completed the well-validated, self-rated Childhood Trauma Questionnaire (CTQ), as well as measures of SAD symptom severity and quality of life. RESULTS: Fully 70% (n=72) of the GSAD sample met severity criteria for at least one type of childhood abuse or neglect as measured by the CTQ subscales using previously established thresholds. CTQ total score adjusted for age and gender was associated with greater SAD severity, and poorer quality of life, function, and resilience. Further, the number of types of maltreatment present had an additive effect, with specific associations for emotional abuse and neglect with SAD severity. CONCLUSIONS: Despite the use of validated assessments, our findings are limited by the retrospective and subjective nature of self-report measures used to assess childhood maltreatment. Nonetheless, these data suggest a high rate of childhood maltreatment in individuals seeking treatment for GSAD, and the association of maltreatment with greater disorder severity suggests that screening is clinically prudent.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".