Differences in Demographic, Risk, and Protective Factors in a Clinical Sample of Children who Experienced Sexual Abuse Only vs. Poly-victimization
Bibliographic record
Abstract
Children exposed to child sexual abuse (CSA) vary considerably with regards to their presenting concerns and treatment needs. One factor creating heterogeneity amongst children experiencing CSA is their history of experiencing other victimizations (i.e., poly-victimized or not). However, little is known about risk factors for poly-victimization as well as differences in protective factors among these two groups. Additionally, there is currently limited understanding of whether poly-victimization is associated with greater trauma symptoms in children exposed to CSA and being seen for trauma treatment. Using a clinical sample of 117 children who were sexually abused (64 CSA only and 53 poly-victimized) ranging from age 3–18 years, the current study examined demographic characteristics, abuse characteristics, trauma symptoms, and protective factors using casefile review methodology. After accounting for other risk factors, parental abuse history and protective factors were significantly associated with child poly-victimization status. Children exposed to poly-victimization were more likely to have financial concerns χ(1,115)2 = 4.16, p = 0.04, parents with abuse histories χ(1,117)2 = 8.93, p = 0.003, and parents with histories of mental health or substance use difficulties χ(1,117)2 = 4.02, p = 0.045. Although cumulative trauma symptoms scores were higher for children who were poly-victimized compared to CSA only, t(115) = −2.24, p = 0.03, multiple regression analyses showed that poly-victimization status was not significantly associated with child trauma symptoms after accounting for other demographic and abuse characteristics. Assessing and understanding the extent to which children exposed to CSA have experienced other forms of maltreatment is critical for identifying children who may be most at risk of poor 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".