Latent class analysis of post-traumatic stress symptoms and complex PTSD in child victims of sexual abuse and their response to Trauma-Focused Cognitive Behavioural Therapy
Bibliographic record
Abstract
Background: PTSD symptoms are frequent in child victims of sexual abuse. Yet, authors have argued that early trauma could lead to alterations in development that go far beyond the primary symptoms of PTSD and have proposed Complex PTSD as an alternative diagnosis encompassing difficulties in affect regulation, relationships and self-concept.Objective: To delineate profiles in child victims of sexual abuse and explore whether profiles are associated with treatment response to Trauma-Focused Cognitive Behavioural Therapy.Method: Latent class analysis was used to identify symptom profiles at baseline assessment of 384 children ages 6 to 14, recruited in a Child Advocacy Centre following disclosure of sexual abuse. Dimensions of Complex PTSD diagnosis as proposed by the ICD-11 were derived from self-report questionnaires.Results: Latent class analysis identified a best fitting model of three classes: Classic PTSD regrouping 51% of children, Complex PTSD describing 23% of children, and Resilient describing 25% of children. Trauma-focused therapy was associated with a significant reduction of dissociation, internalizing, and externalizing problems for children of all three classes. Trauma-focused therapy was also linked to a significant reduction of PTSD symptoms with larger effect size (d = .90; 95%CI: 0.63–1.16) for children classified in the Complex PTSD class.Conclusion: These findings highlight the utility of a person-oriented approach to enhance our understanding of the diversity of profiles in child victims. The results offer empirical support for the ICD-11 PTSD and Complex PTSD distinction in a clinical sample of sexually abused children and the relevance of this distinction in foreseeing treatment outcomes.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".