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Record W2031293797 · doi:10.1300/j229v07n03_03

Dissociative Experiences in China

2006· article· en· W2031293797 on OpenAlexaboutno aff
Zeping Xiao, Zhen Wang, Zheng Zhou, Yong Xu, Jue Cheng, Haiyin Zhang, Colin A. Ross, Benjamin B. Keyes

Bibliographic record

VenueJournal of Trauma & Dissociation · 2006
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsSexual abuseDissociative disordersDissociativeDissociative Experiences ScalePsychiatryPsychologyPopulationChild abuseClinical psychologyPhysical abusePoison controlSuicide preventionDemographyMedicineMedical emergency

Abstract

fetched live from OpenAlex

The Dissociative Experiences Scale was administered to a non-clinical sample in Shanghai, China (N = 618) and the results were compared with a previous sample of the general population from Winnipeg, Canada (N = 1055). The Dissociative Disorders Interview Schedule was administered to the 618 Chinese participants and results were compared with those of the Canadian participants (N = 502). In addition, both measures were administered to a sample of Chinese psychiatric inpatients (N = 423) and outpatients (N = 304). Rates of childhood trauma and dissociation were far lower in the Chinese non-clinical sample than in the two Chinese psychiatric patient groups, and far lower than in the Canadian general population. Among the 618 respondents in the Chinese non-clinical sample, no childhood sexual abuse was reported and only one person reported childhood physical abuse. These rates of childhood abuse were far lower than in other non-clinical samples from China; for example, rates were 16.7% for sexual abuse of girls and 10.5% for sexual abuse of boys in a previous study. Among the more traumatized Chinese psychiatric patients, and among the Canadian respondents, dissociative experiences were much more common than in the Chinese general population. The data provide a base frequency for dissociation in non-clinical samples reporting little or no childhood physical and sexual abuse.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.033
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.278
Teacher spread0.270 · 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 teacher head, 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

Citations17
Published2006
Admission routes1
Has abstractyes

Explore more

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