Trauma-Related Altered States of Consciousness: Exploring the 4-D Model
Bibliographic record
Abstract
Frewen and Lanius (in press) recently articulated a 4-D model as a framework for classifying symptoms of posttraumatic stress into those that potentially occur within normal waking consciousness (NWC) versus those that intrinsically represent dissociative experiences of trauma-related altered states of consciousness (TRASC). Four dimensions were specified: time-memory, thought, body, and emotion. The 4-D model further hypothesizes that in traumatized persons, symptoms of TRASC, compared with NWC forms of distress, will be (a) observed less frequently; (b) less intercorrelated, especially as measured as moment-to-moment states; (c) observed more frequently in people with high dissociative symptomatology as measured independently; and (d) observed more often in people who have experienced repeated traumatization, particularly early developmental trauma. The aim of the present research was to begin to evaluate these 4 predictions of the 4-D model. Within a sample of 74 women with posttraumatic stress disorder (PTSD) primarily due to histories of childhood trauma, as well as within a 2nd sample of 504 undergraduates (384 females), the 1st 2 hypotheses of the 4-D model were supported. In addition, within the PTSD sample, the 3rd hypothesis was supported. However, inconsistent with the 4th hypothesis, severity of childhood trauma history was not strongly associated with TRASC. We conclude that the hypotheses articulated by the 4-D model were generally supported, although further research in different trauma-related disorders is needed, and the role of childhood trauma history in the etiology of TRASC requires further research.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".