EVIDENCE FOR A DISSOCIATIVE SUBTYPE OF PTSD BY LATENT PROFILE AND CONFIRMATORY FACTOR ANALYSES IN A CIVILIAN SAMPLE
Bibliographic record
Abstract
BACKGROUND: Dissociative symptoms are increasingly recognized in individuals with posttraumatic stress disorder (PTSD). The aim of this study was to investigate the prevalence of derealization and depersonalization symptoms via latent profile analyses (LPAs) in a civilian PTSD sample and examine the relationship between PTSD and dissociative symptoms via factor analytic methods. METHODS: A civilian sample of individuals with PTSD predominantly related to childhood abuse (n = 134) completed a diagnostic interview for PTSD and comorbid psychiatric disorders. LPAs and confirmatory factor analyses (CFAs) were performed on the severity scores for PTSD, derealization, and depersonalization symptoms. RESULTS: LPAs extracted three groups, one of which was uniquely characterized by high derealization and depersonalization symptoms, and accounted for 25% of the sample. Individuals in the dissociative subgroup also showed a higher number of comorbid Axis I disorders and a more significant history of childhood abuse and neglect. CFAs suggested the acceptance of a five factor solution in which dissociative symptoms are distinct from but correlate significantly with the core PTSD symptom clusters. CONCLUSIONS: The results from LPAs and CFAs are concordant with the concept of a dissociative subtype in patients with PTSD and suggest that symptoms of derealization-depersonalization and the core symptoms of PTSD are positively correlated. Thought should be given to including a dissociative subtype of PTSD in the DSM-5.
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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 | 0.004 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".