Double Jeopardy: Risk Assessment in the Context of Child Maltreatment and Domestic Violence
Bibliographic record
Abstract
Investigations of child maltreatment often involve domestic violence, but there is little guidance about how to properly assess risk in such cases. Empirically validated risk assessment tools have been used successfully in childwelfare and, to a lesser extent, in cases involving domestic violence, but these have generally not been utilized in tandem. Using the allegation of child maltreatment as the entry point for services, this paper proposes a nested risk assessment framework whereby risk of both child maltreatment and domestic violence are considered simultaneously using two different standardized instruments. [Brief Treatment and Crisis Intervention 7:253–274 (2007)] KEY WORDS: child abuse, domestic violence, risk assessment. Responding to child maltreatment (CM) is far more complicated than keeping children ‘‘safe’’ or ‘‘protected’ ’ from their own parents. The twin goals of safety and permanence imply that caseworkers must consider both the safety and ultimate well-being of the child. That is, at each decision point, caseworkers must weigh the po-tential for harm if nothing is done (i.e., leaving the child in a potentially abusive home) with the risk that intrusive actions aimed at child protection will, ultimately, prove to be harmful (i.e., unnecessarily separating a child from their parent). This is no simple equation and the stakes are high. Yet the combination of severe consequences, the inherent difficulty of mak-ing accurate assessments, and differences in skill levels among Children’s Protective Serv-ices (CPS) workers is a set-up for unreliable case
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".