Evaluation of the evidence for the trauma and fantasy models of dissociation.
Bibliographic record
Abstract
The relationship between a reported history of trauma and dissociative symptoms has been explained in 2 conflicting ways. Pathological dissociation has been conceptualized as a response to antecedent traumatic stress and/or severe psychological adversity. Others have proposed that dissociation makes individuals prone to fantasy, thereby engendering confabulated memories of trauma. We examine data related to a series of 8 contrasting predictions based on the trauma model and the fantasy model of dissociation. In keeping with the trauma model, the relationship between trauma and dissociation was consistent and moderate in strength, and remained significant when objective measures of trauma were used. Dissociation was temporally related to trauma and trauma treatment, and was predictive of trauma history when fantasy proneness was controlled. Dissociation was not reliably associated with suggestibility, nor was there evidence for the fantasy model prediction of greater inaccuracy of recovered memory. Instead, dissociation was positively related to a history of trauma memory recovery and negatively related to the more general measures of narrative cohesion. Research also supports the trauma theory of dissociation as a regulatory response to fear or other extreme emotion with measurable biological correlates. We conclude, on the basis of evidence related to these 8 predictions, that there is strong empirical support for the hypothesis that trauma causes dissociation, and that dissociation remains related to trauma history when fantasy proneness is controlled. We find little support for the hypothesis that the dissociation-trauma relationship is due to fantasy proneness or confabulated memories of trauma.
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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 | 0.018 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".