Unveiling Suicidal Risk in Young Child Sexual Abuse Victims: Prevalence and Predictive Markers
Bibliographic record
Abstract
Child sexual abuse has repeatedly been identified as a risk factor for suicidal ideation and behavior, yet most research has focused on adolescents and young adults. Very little is known about suicidality in children exposed to sexual abuse in middle childhood, which is a developmental period marked by unique cognitive and socioemotional specificities. Gaining a better understanding of risk factors in this subgroup is crucial to inform age-appropriate prevention and intervention efforts. This study aimed to: 1) determine the prevalence of suicidal ideation and behavior in a sample of child victims of sexual abuse aged 6-12 years old, using both self- and parent-reports and 2) model a regression tree to identify the most potent markers of suicidal risk. A total of 783 children aged 6-12, and their non-offending caregivers, completed questionnaires on suicidality, and correlates of suicidal risk (e.g. depression, emotional regulation, post-traumatic stress symptoms, and perceived maternal support following disclosure of abuse). The prevalence of suicidal ideation was 31.2% and 11.4% according to children and parents, respectively. Findings revealed that emotional dysregulation and clinical levels of depression were the most influential variables in the prediction of suicide risk. Namely, the subgroup that showed the highest suicidal risk consisted of children who had both high levels of emotional dysregulation and clinical levels of depression. The decision tree model offers an important screening tool for clinicians wishing to identify children most at-risk of suicidality.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".