Trauma-related altered states of consciousness in women with BPD with or without co-occurring PTSD
Bibliographic record
Abstract
BACKGROUND: A "4-D model" was recently described as a theoretical framework for categorizing trauma-related symptoms into four phenomenological dimensions (the experience of time, thought, body, and emotion) that can present either in the form of normal waking consciousness (NWC) or as dissociative experiences, that is, trauma-related altered states of consciousness (TRASC). METHODS: The present study examined the predictions of the 4-D model in 258 persons with borderline personality disorder (BPD) with (n=126) versus without (n=132) posttraumatic stress disorder (PTSD). RESULTS: As measured by the Borderline Symptom List, consistent with the predictions of the 4-D model, in comparison with symptom endorsements theorized to be associated with NWC, measures of TRASC were less frequent, and more strongly correlated with both Dissociative Experience Scale scores and severity of childhood emotional neglect, particularly in persons with both BPD and PTSD. Our prediction that symptoms of TRASC would be less intercorrelated in comparison with distress associated with NWC symptoms, however, was not supported. CONCLUSIONS: Findings are discussed as they pertain to the symptomatology of BPD, PTSD, and dissociation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".