Identifying PTSD and Complex PTSD Profiles in Child Victims of Sexual Abuse
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".