Correlates and Predictors of Mothers’ Adaptation and Trauma Symptoms Following the Unveiling of the Sexual Abuse of Their Child
Bibliographic record
Abstract
Child sexual abuse (CSA) can severely affect the mental health of children and their parents. While correlates of recovery have been documented in children, factors exacerbating parents' adaptation to their child's unveiling of CSA deserves further attention. Parents' history of abuse has been inconsistently identified as a predictor of their distress in reaction to their child's abuse disclosure. This study proposes a mediation model that explores various processes underlying mother's psychological distress (posttraumatic stress disorder [PTSD], dissociation, and their comorbidity) following their children's unveiling of CSA. It investigates the influence of mother's own CSA, as well as of her exposure to additional forms of past and current victimization, on her reaction to the child's CSA disclosure, while considering coping mechanisms as mediators (avoidance, problem solving, search for social support, and feeling of guilt). Data were collected through self-report measures completed by 298 mothers of children who had recently disclosed CSA. Path analyses revealed that mother's exposure to interparental violence as a child acted as a primary predictor of dissociation and of its comorbidity with PTSD, while a history of CSA was directly and exclusively linked to dissociation. Being exposed to recent partner violence was indirectly related to trauma symptoms, with coping mechanisms acting as mediators. This study outlines the relationship between mother's psychological distress and her cumulative, past, and current exposure to various forms of victimization. Exposure to interparental violence as a child represents a particularly important factor for identifying mothers most in need of support, as it is a significant predictor of dissociation and of its comorbidity with PTSD.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".