Childhood Maltreatment and Revictimization in a Homeless Population
Bibliographic record
Abstract
We examined the hypothesis that exposure to childhood maltreatment increases the vulnerability to Adult Victimization (AV) in a homeless population (N = 500). We also investigated the effects of specific types (emotional, physical, and sexual) and cumulative experience of childhood maltreatment on AV, and whether gender moderates these relationships. All three groups with AV experience (emotional, physical, and sexual) indicated higher exposure to childhood abuse and cumulative maltreatment, and those who were sexually victimized as an adult showed higher exposure to childhood neglect. In addition, exposure to childhood maltreatment had type-specific and cumulative effects on AV. Exposure to all types of childhood abuse maintained a strong direct association with AV, regardless of demographic characteristics, including age, ethnicity, marital status, education level, and housing situation. In addition, exposure to physical neglect showed a significant relationship with Adult Sexual Victimization. Cumulative experience of childhood maltreatment was consistently associated with cumulative risk of experiencing AV. Gender had no significant effect on these relationships. Findings suggest that intervention programs in homeless population should consider the history of childhood maltreatment and its characteristics to increase the effectiveness of intervention strategies for AV in this population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".