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Record W2413120769 · doi:10.1097/nmd.0000000000000303

Dissociation in Individuals Denying Trauma Exposure

2015· article· en· W2413120769 on OpenAlexaff
John Briere, Marsha Runtz

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2015
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDissociation (chemistry)PsychologyAffect (linguistics)Clinical psychologyEmotional dysregulationDistressPopulationDissociativeAffect regulationEmotional distressEmotional regulationDevelopmental psychologyAnxietyPsychiatryMedicineEnvironmental healthChemistry

Abstract

fetched live from OpenAlex

A number of studies suggest that dissociation is reliably related to trauma exposure, and that inadequate regulation of posttraumatic distress may be a significant factor. We examined whether affect dysregulation predicts dissociation in those denying any lifetime exposure to trauma. These relationships were evaluated in a general population sample and a second sample of nontraumatized university students. In the first study, multivariate analyses indicated that, along with gender, affect dysregulation was a relatively strong predictor, accounting for 27% of the variance in dissociation. In the replication study, dissociation was associated with affect dysregulation, but not gender. Affect dysregulation seems to predict dissociative symptomatology in nontraumatized individuals. It is hypothesized that emotional distress, whether from trauma or other etiologies, motivates dissociation to the extent that it challenges the individual's compromised capacity for affect regulation. Treatment implications may include the potential helpfulness of interventions that increase emotion regulation skills.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.027
GPT teacher head0.292
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2015
Admission routes1
Has abstractyes

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Same venueThe Journal of Nervous and Mental DiseaseSame topicPsychosomatic Disorders and Their TreatmentsFrench-language works237,207