Validity of CBCL-Derived PTSD and Dissociation Scales
Bibliographic record
Abstract
There is growing evidence that child neglect is an important risk factor for posttraumatic stress disorder (PTSD) and dissociation. Considering that the Child Behavior Checklist (CBCL) is a widely used measure, the possibility of using validated CBCL-derived trauma symptoms scales could be particularly useful to better understand how trauma symptoms develop among neglected children and adolescents. This study examined the factor structure of three CBCL-derived measures of PTSD and dissociation (namely, PTSD scale, Dissociation scale, and PTSD/Dissociation scale) in a sample of 239 neglected children and adolescents aged 6 to 18 years using the latest version of CBCL (CBCL 6-18). Evidence of convergent validity of these scales was also examined for participants aged 12 and under using two well-validated measures of PTSD and Dissociation: the Trauma Symptoms Checklist for Young Children and the Child Dissociation Checklist. Findings suggest that CBCL-derived measures of trauma symptoms, especially PTSD and Dissociations scales, may be of heuristic value in the study of trauma symptomatology in neglected samples. Factor structure and evidence of convergent validity were supported for these two scales. Results also provide further support to the well-established assumption that PTSD and dissociation are two related but different constructs.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".