Sexual, Physical, and Emotional Maltreatment in Childhood Are Differentially Associated With Sexual and Physical Revictimization in Adulthood
Bibliographic record
Abstract
Childhood maltreatment increases risk for sexual and physical revictimization in adulthood. The goal of the current study was to examine whether this risk is associated with specific maltreatment types (i.e., sexual vs. physical vs. emotional maltreatment vs. neglect) and perpetrators (i.e., mother vs. father). Participants included 720 adult women from North America and the United Kingdom, recruited through the online platform Prolific Academic. The severity of childhood maltreatment and adult physical and sexual victimization were assessed in two separate sessions through self-report questionnaires. All maltreatment types were modeled together to account for their co-occurrence. Greater severity of sexual maltreatment was significantly and independently associated with greater risk for sexual, physical, and sexual + physical revictimization. Further, in the full sample, risk of revictimization was predicted by greater severity of father-perpetrated emotional and physical maltreatment. In contrast, in subgroup analyses focusing on plurisexual (i.e., bi/pansexual) women, risk of revictimization was predicted by greater severity of mother-perpetrated emotional and physical maltreatment. These results suggest that girls with sexual and emotional maltreatment histories are at highest risk for revictimization. Future research identifying the biological, psychological, and social sequelae of these specific exposures may enable the development of specific intervention programs that have the potential for maximum efficacy in preventing further violence against women most at risk.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".