MétaCan
Menu
Back to cohort
Record W2972866935 · doi:10.1097/nmd.0000000000001057

Predictors of Dissociative Experiences Among Adolescents in Nepal

2019· article· en· W2972866935 on OpenAlexaff
Ram P. Sapkota, Alain Brunet, Laurence J. Kirmayer

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2019
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsJewish General HospitalMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsDissociativeDissociative Experiences ScaleDissociation (chemistry)Dissociative disordersPsychogenic diseasePsychologyCognitionClinical psychologyDistressPath analysis (statistics)PersonalityDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

In recent years, many adolescents in Nepal have been affected by episodes of mass psychogenic illness, which seem to involve dissociative symptoms. To identify the potential contributors to dissociation, the present study examined correlates of dissociative experiences among adolescents in Nepal. In a cross-sectional survey, 314 adolescents were assessed with the Adolescent Dissociative Experiences Scale and measures of childhood trauma exposure, as well as cognitive and personality traits found to be associated with dissociation in studies on other populations. Path analysis confirmed that childhood trauma, cognitive and personality traits, and current distress each predicted dissociative experiences and behaviors. However, an integrated path model found that the effect of childhood trauma on dissociation was mediated either by posttraumatic stress symptoms or by cognitive failures. Future studies should develop and test multifactorial models of dissociation and multiple pathways.

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)0.001
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.249
Teacher spread0.243 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

Explore more

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