Dissociation in Psychiatric Disorders: A Meta-Analysis of Studies Using the Dissociative Experiences Scale
Bibliographic record
Abstract
OBJECTIVE: Dissociation is a complex, ubiquitous construct in psychopathology. Symptoms of dissociation are present in a variety of mental disorders and have been connected to higher burden of illness and poorer treatment response, and not only in disorders with high levels of dissociation. This meta-analysis offers a systematic and evidence-based study of the prevalence and distribution of dissociation, as assessed by the Dissociative Experiences Scale, within different categories of mental disorders, and it updates an earlier meta-analysis. METHOD: More than 1,900 original publications were screened, and 216 were included in the meta-analysis, comprising 15,219 individuals in 19 diagnostic categories. RESULTS: The largest mean dissociation scores were found in dissociative disorders (mean scores >35), followed by posttraumatic stress disorder, borderline personality disorder, and conversion disorder (mean scores >25). Somatic symptom disorder, substance-related and addictive disorders, feeding and eating disorders, schizophrenia, anxiety disorder, OCD, and most affective disorders also showed mean dissociation scores >15. Bipolar disorders yielded the lowest dissociation scores (mean score, 14.8). CONCLUSIONS: The findings underline the importance of careful psychopathological assessment of dissociative symptoms in the entire range of mental disorders.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".