Dissociation in victims of childhood abuse or neglect: a meta-analytic review
Bibliographic record
Abstract
Childhood abuse and neglect are associated with dissociative symptoms in adulthood. However, empirical studies show heterogeneous results depending on the type of childhood abuse or neglect and other maltreatment characteristics. In this meta-analysis, we systematically investigated the relationship between childhood interpersonal maltreatment and dissociation in 65 studies with 7352 abused or neglected individuals using the Dissociative Experience Scale (DES). We extracted DES-scores for abused and non-abused populations as well as information about type of abuse/neglect, age of onset, duration of abuse, and relationship to the perpetrator. Random-effects models were used for data synthesis, and meta-regression was used to predict DES-scores in abused populations from maltreatment characteristics. The results revealed higher dissociation in victims of childhood abuse and neglect compared with non-abused or neglected subsamples sharing relevant population features (MAbuse = 23.5, MNeglect = 18.8, MControl = 13.8) with highest scores for sexual and physical abuse. An earlier age of onset, a longer duration of abuse, and parental abuse significantly predicted higher dissociation scores. This meta-analysis underlines the importance of childhood abuse/neglect in the etiology of dissociation. The identified moderators may inform risk assessment and early intervention to prevent the development of dissociative symptoms.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".