The Dissociative Subtype of PTSD Interview (DSP-I): Development and Psychometric Properties
Bibliographic record
Abstract
(DSP-I). This clinician-administered instrument assesses the presence and severity of PTSD-DS (i.e., symptoms of depersonalization or derealization) and contains a supplementary checklist that enables assessment and differentiation of other trauma-related dissociative symptoms (i.e., blanking out, emotional numbing, alterations in sensory perception, amnesia, and identity confusion). The psychometric properties were tested in 131 treatment-seeking individuals with PTSD and histories of multiple trauma, 17.6 % of whom met criteria for PTSD-DS in accordance with the DSP-I. The checklist was tested in 275 treatment-seeking individuals. Results showed the DSP-I to have high internal consistency, good convergent validity with PTSD-DS items of the CAPS-5, and good divergent validity with scales of somatization, anxiety and depression. The depersonalization and derealization scales were highly associated. Moreover, the DSP-I accounted for an additional variance in PTSD severity scores of 8% over and above the CAPS-5 and number of traumatic experiences. The dissociative experiences of the checklist were more strongly associated with scales of overall distress, somatization, depression, and anxiety than scales of depersonalization and derealization. In conclusion, the DSP-I appears to be a clinically relevant and psychometrically sound instrument that is valuable for use in clinical and research settings.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".