PTSD, social anxiety disorder, and trauma: An examination of the influence of trauma type on comorbidity using a nationally representative sample
Bibliographic record
Abstract
Posttraumatic stress disorder (PTSD) and social anxiety disorder (SAD) are highly comorbid (Collimore et al., 2010). Trauma may present a shared environmental factor contributing to the development of comorbidity; however, existent research has been hampered by use of restrictive samples and limitations in the range of traumas investigated. The current study examines the relationship between a broad range of potentially traumatic events and the comorbidity between PTSD and SAD using Wave 2 of the National Epidemiological Survey of Alcohol and Related Conditions (n=34,653). Multiple logistic regressions and cross-tabulations were used to evaluate differences in the prevalence of potentially traumatic events among those who met criteria for comorbid PTSD-SAD compared to those with PTSD without SAD and SAD without PTSD. Those in the comorbid PTSD-SAD group were significantly more likely than those in the PTSD without SAD or SAD without PTSD groups to report experiencing specific types of assaultive violence, childhood maltreatment, and other shocking events. Associations between comorbidity and childhood maltreatment were significant for females only. Individuals diagnosed with comorbid PTSD-SAD are more likely than those diagnosed with either disorder alone to report exposure to specific types of traumatic events within their lifetime.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".