Risk Factors for Maltreatment in Siblings of Abused Children
Bibliographic record
Abstract
OBJECTIVES: To examine the association between child maltreatment (abuse and neglect) in one sibling and that in another as well as associated risk factors. METHODS: = 1040). Exposure to suspected child maltreatment was measured by linkage with state child protection agency data. Self-reports of childhood sexual abuse were also collected at the 21-year follow-up. RESULTS: = 44). A notification in the first sibling was associated with a 60-fold increase in the likelihood of a notification in the second sibling (95% confidence interval: 29.3-125.1), resulting in nearly three-quarters being the subject of a report. In terms of the subtypes, neglect revealed the strongest association, followed by sexual abuse. At the 21-year follow-up, 58% of second siblings reported sexual abuse when the first sibling disclosed similar experiences. On adjusted analyses, maternal age of <20 years was the strongest and most consistent predictor of abuse, with indigenous status, maternal depression, parental relationship, and familial poverty playing a lesser role. CONCLUSIONS: Our results highlight the close association between child abuse in one sibling and maltreatment in a second sibling as well as possible risk factors. Greater awareness of these factors may inform interventions, particularly primary and secondary prevention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".