Factors Associated with Pathological Dissociation in the General Population
Bibliographic record
Abstract
OBJECTIVE: This study assessed the prevalence of pathological dissociation in the general population, and the relationship between pathological dissociation and sociodemographic and several psychiatric variables. METHOD: The stratified population sample consisted of 2001 subjects. The study questionnaires included the Dissociative Experiences Scale, the Dissociative Experiences Scale-Taxon, the Toronto Alexithymia Scale, the Beck Depression Inventory, and sociodemographic background. RESULTS: The prevalence of pathological dissociation (DES-T >/= 20) was 3.4% in the general population and did not differ significantly between genders. Men scored higher than women in the amnesia subscale, and women in the absorption and imaginative involvement subscale. The relationship between pathological dissociation, alexithymia, depression and suicidality was strong. The likelihood of pathological dissociation was nearly nine-fold higher among depressive subjects, more than seven-fold higher among alexithymic subjects, and more than four-fold higher among suicidal subjects than among the others. Frequent alcohol consumption also associated significantly with pathological dissociation. CONCLUSIONS: A significant relationship between pathological dissociation, depression, alexithymia, and suicidality was found in the general population. The importance of these factors should be examined in a prospective study design to determine causality.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".