Survival, Attachment, and Healing: An Evolutionary Lens on Interventions for Trauma-Related Dissociation
Bibliographic record
Abstract
Purpose: Dissociation is a necessary part of our threat response system, common to all animal species, normally temporarily activated under conditions of extreme or inescapable threat. Pathological dissociation, however, continues to occur after the initial threat has passed, in response to reminders or inaccessibility of safety and security. Present across the spectrum of psychiatric diagnoses, recurrent dissociative symptoms are linked to severe trauma exposure, insecure attachment, treatment non-response, and maladaptive coping behaviors such as substance use, suicidality, and self-harm. However, empirical studies testing treatments specific to dissociative processes remain scarce. This narrative review summarizes existing studies and provides theoretical, neurobiological, and evolutionary perspectives on dissociative processes and treatments for pathological dissociation. Methods: A systematic search of five databases (MEDLINE, EMBASE, APA PsycINFO, CINAHL plus, Scopus) was conducted on April 13, 2023. Peer-reviewed clinical studies with adult participants, assessing intervention effects on dissociative symptoms, were included. Results were thematically analyzed and summarized. Results: Sixty-nine studies were identified, mainly focused on posttraumatic stress disorder, trauma-exposed populations, and borderline personality disorder. Psychotherapy was studied in 72.5% of studies; other interventions included medications and neurostimulation. The majority reported positive outcomes, despite the heterogeneous spectrum of interventions. However, treatment of dissociative symptoms was the primary objective in only a minority. Conclusion: Pathological dissociation is a complex phenomenon involving brain and body systems designed for perceiving and responding to severe threats, requiring an individualized approach. A literature is emerging regarding potentially evidence-based treatments to help those impacted by recurrent dissociative symptoms. When contextualized within a neurobiological and evolutionary perspective, these treatments can be understood as facilitating an internal and/or relational sense of safety, resulting in symptom reduction. Further studies are needed to explore effective treatments for 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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".