Emotional Intelligence in Female Children with PTSD After Sexual Abuse
Bibliographic record
Abstract
Objectives: The aim was to investigate the effects of child sexual abuse on emotional intelligence in children with posttraumatic stress disorder (PTSD). Also, there is no study assessing the effects of incest on emotional intelligence in children with PTSD after sexual abuse. The current study aimed to assess the effects of incest on emotional intelligence. Methods: This study included 30 female children with sexual abuse, 20 female traffic accident victims with PTSD, and 25 female healthy volunteers as controls. All participants in the study were assessed with the Toronto Alexithymia Scale (TAS-20) and the Difficulties in Emotion Regulation Scale (DERS) and the reading mind from eyes test (RMET) for children. Results: It was found that the TAS-20 total score was significantly higher in the CSA victims than in controls. Difficulty identifying feelings and difficulty describing feelings subscales scores were higher in the incest group. When the groups were compared regarding total DERS scores, it was found that it was found that the DERS total scale was significantly higher in the CSA victims than in controls while no difference was detected between the CSA victims without incest and the incest group. RMET score was significantly lower in the incest group than in the CSA victims without incest. Conclusion: Sexual abuse disrupts emotional processing in female children. It is important to consider the clinical features of emotion processing that could contribute to PTSD treatment in children with sexual abuse.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".