Narratives of Dissociation: Insights into the Treatment of Dissociation in Individuals Who Were Sexually Abused as Children
Bibliographic record
Abstract
This article provides an in-depth understanding of the treatment of dissociation from the perspective of 7 individuals who have a history of childhood sexual abuse and engage in moderate to high levels of dissociative behavior. Levels of dissociation were screened using the Dissociative Experiences Scale (E. Bernstein-Carlson & F. Putnam, 1993 Bernstein-Carlson, E. and Putnam, F. 1993. An update on the Dissociative Experiences Scale. Dissociation, 4(1): 16–26. [Google Scholar]), and individual interviews were conducted to gain insight into how dissociation can best be treated in a therapeutic context. From a narrative research design with a holistic-content analysis (A. Lieblich, R. Tuval-Mashiach, & T. Zilber, 1998 Lieblich, A., Tuval-Mashiach, R. and Zilber, T. 1998. “Narrative research: Reading, analysis, and interpretation”. In Applied Social Research Methods Series, 47, Thousand Oaks, CA: Sage. [Google Scholar]), 3 major themes and 16 subthemes were revealed. The major themes included (a) identifying specific tools and techniques that were recognized as critical in managing dissociative symptoms, (b) challenging the dominant medical paradigm by underscoring the importance of helping clients contextualize and normalize dissociative behavior, and (c) highlighting specific characteristics of the therapeutic relationship that create the necessary trust and safety to facilitate reparation. A detailed description of the 3 major themes and 16 subthemes is provided. Implications for both clinical theory and practice are identified.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| 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".