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Record W4402689813 · doi:10.1080/10538712.2024.2403996

Identifying PTSD and Complex PTSD Profiles in Child Victims of Sexual Abuse

2024· article· en· W4402689813 on OpenAlexafffund
Martine Hébert, Laetitia Mélissande Amédée, Amélie Tremblay‐Perreault

Bibliographic record

VenueJournal of Child Sexual Abuse · 2024
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversité du Québec à Montréal
FundersCanadian Institutes of Health Research
KeywordsSexual abuseChild sexual abusePsychologyChild abuseClinical psychologyPsychiatryPoison controlSuicide preventionMedicineMedical emergency

Abstract

fetched live from OpenAlex

Post-traumatic stress disorder (PTSD) symptoms are frequent in child victims of sexual abuse. Authors argued that early trauma could lead to alterations in development that go far beyond the primary symptoms of PTSD and have proposed that Complex PTSD (C-PTSD) involving alterations in attachment, biology, affect regulation, consciousness, behavioral regulation, cognition, and self-concept, may better describe children experiencing chronic trauma at an early developmental stage. The aim of the study was to disentangle the diversity of profiles in child victims of sexual abuse based on the C-PTSD framework. Latent profile analysis was used to identify distinct subgroups in a sample of 861 sexually abused children aged 6 to 12. Children and their non-offending parents completed questionnaires evaluating PTSD symptoms and measures documenting alterations in development characteristics of C-PTSD. Latent profile analysis identified a best-fitting model consisting of three profiles: PTSD (40.7% of children), Resilient (32.8% of children), and C-PTSD (26.5% of children). Compared to others, children in the C-PTSD profile were more likely to have experienced more forms of interpersonal trauma and showed impairments in several domains. Findings underscore the importance of tailoring interventions to efficiently address the needs of young victims of sexual trauma.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.041
GPT teacher head0.329
Teacher spread0.288 · 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.

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
Published2024
Admission routes2
Has abstractyes

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