Validation of the Peritraumatic Dissociative Experiences Questionnaire and Peritraumatic Distress Inventory in school-aged Victims of Road Traffic Accidents
Bibliographic record
Abstract
BACKGROUND: Although the reliable and valid Peritraumatic Distress Inventory (PDI-C) and Peritraumatic Dissociative Experiences Questionnaire (PDEQ) are useful for identifying adults at risk of developing acute and chronic posttraumatic stress disorder (PTSD), they have not been validated in school-aged children and their predictive values remain unknown in this population. This study aims to assess the psychometric properties of the children versions of these two measures (PDI-C and PDEQ-C) in a sample of French-speaking school-children. METHODS: One-hundred and thirty-three consecutive victims of road traffic accidents, aged 8-15 years, were recruited into this longitudinal study via the emergency room. The peritraumatic reactions were assessed at baseline and PTSD symptoms were assessed 1 month later. RESULTS: Cronbach's alpha coefficients were 0.8 and 0.77 for the PDI-C and PDEQ-C, respectively. The 1-month test-retest correlation coefficient (n=33) was 0.77 for both measures. The PDI-C demonstrated a two-factor structure while the PDEQ-C displayed a one-factor structure. As with adults, the two measures were intercorrelated (r=0.52) and correlated with subsequent PTSD symptoms and diagnosis (r=0.21-0.56; P<0.05). CONCLUSIONS: The children versions of the PDI and PDEQ are reliable and valid in children.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".