Developmental and attachment-based perspectives on dissociation: beyond the effects of maltreatment
Bibliographic record
Abstract
Background: Numerous years of theory and research have informed our understanding of the caregiving experiences that confer vulnerability for dissociation. This work has resulted in widespread agreement on the role of childhood maltreatment as an aetiological factor.Objective: With clear integration of this perspective, the current paper draws attention to the spectrum of vulnerability that can exist over and above the trauma of maltreatment within early caregiving experiences.Method: An integrative review of the developmental literature on dissociation is presented.Results: We first review and integrate existing developmental theories of dissociation into a more unified perspective, highlighting a combination of defensive and intersubjective pathways towards dissociative outcomes. Next, we present empirical research demonstrating which specific caregiving experiences are associated with dissociation. Lastly, we review recent neurodevelopmental research demonstrating that (non-extreme) caregiving stressors during infancy impact the developing limbic structures in the brain. We conclude by offering directions for future research.Conclusion: Findings make the case for approaching assessments of the caregiver-child relationship with discernment of factors beyond the presence/absence of maltreatment when conceptualizing risk pathways toward dissociation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".