The Associations between Self-Silencing and Delinquency in Adolescent Who Experienced Child Sexual Abuse
Bibliographic record
Abstract
A large body of research reveals that child sexual abuse is an adverse experience associated with many negative socioemotional and behavioral consequences during adolescence. Notably, adolescent victims of child sexual abuse are more likely to engage in delinquent behavior, which has importance for their adaptation later in life. While research on the psychological antecedents of delinquent behavior has independently considered each core feature of silencing the self (i.e., low assertiveness and self-expression, conflict avoidance, presenting a non-authentic self, anger, and hostility), it has failed to jointly consider these as potential predictors of delinquent behaviors in adolescent victims of child sexual abuse. The purpose of this study was to examine the associations between the two dimensions of self-silencing (i.e., divided self and silencing the self) and delinquency in adolescent victims of child sexual abuse. A sample of 110 adolescents seeking services following child sexual abuse completed self-reported questionnaires, and case files were analyzed to gather information regarding child sexual abuse severity. Hierarchical regression analyses revealed that, after controlling for the effect of age, gender, parental monitoring, and child sexual abuse severity, self-silencing was positively associated with delinquent behaviors, affiliation with delinquent peers, as well as alcohol and drug use. Divided self contributed to the prediction of aggressive behaviors. Implementing interventions aimed at increasing adolescents' assertiveness, conflict management skills, and emotion regulation and promoting congruence between their behaviors and authentic selves may lower their involvement in delinquency.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".