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Record W2909129653 · doi:10.1080/15299732.2019.1597814

Peritraumatic dissociation and post-traumatic stress disorder in individuals exposed to armed conflict in the Democratic Republic of Congo

2019· article· en· W2909129653 on OpenAlexaff
Yvonne Duagani Masika, Christophe Leys, Thierry Matonda Ma Nzuzi, Isabelle Blanchette, Samuel Mampunza Ma Miezi, Charles Kornreich

Bibliographic record

VenueJournal of Trauma & Dissociation · 2019
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersAcadémie de recherche et d'enseignement supérieur
KeywordsPsychologyDissociativeClinical psychologyPsychiatryPosttraumatic stressPopulationTraumatic stressMedicine

Abstract

fetched live from OpenAlex

The purpose of this study was to verify the hypothesis that there is an association between peritraumatic dissociation (PD) and post-traumatic stress disorder (PTSD) in individuals exposed to recurrent armed conflict. More specifically, we sought to evaluate whether PD differentially predicts PTSD according to the degree of exposure to the potentially traumatic event (PTE), the level of education, and gender. A total of 120 individuals between 17 and 75 years of age, including 51 women, completed the Traumatic Events List, the Peritraumatic Dissociative Experiences Questionnaire, and the French version of the Posttraumatic Stress Disorder Checklist Scale, as well as a questionnaire providing information regarding sociodemographic details. The group of participants with high scores for PD had significantly more PTSD. PD differentially predicts PTSD depending on the level of education and gender of the individual. Those who had been physically assaulted and raped, as well as the less educated, were more likely to be dissociated during PTE· exposure compared to witnesses and those with a higher level of education. The primary target population for prevention and early management should comprise individuals with high levels of PD, low levels of education, and women.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.037
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.290
Teacher spread0.272 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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