Does differential response make a difference: examining domestic violence cases in child protection services
Bibliographic record
Abstract
Abstract Large numbers of domestic violence (DV) cases on child protection caseloads have necessitated the development of practices that address both DV and child safety. The first step in this process is to gain an understanding of the differences between DV‐involved cases and other forms of maltreatment. The implementation of a differential response service model in Ontario offered an opportunity to compare risk assessment ratings, service outcomes and recurrence and to identify pathways of DV cases through child protection services (CPS). A sample (n = 785) of child protection investigations over a 4‐month period was examined. Of these investigations, 26% cases were DV referred; 87% of the DV victims were mothers; perpetrating partners were mostly absent from investigations; non‐white families were more often investigated for DV than white families; and DV cases were more likely to remain open for ongoing CPS. Only one‐third of DV‐exposed children were assessed as having been harmed and most community referrals were made for the victim parent. Mothers were the primary target of investigation, remaining in CPS for extended service provision although recurrence rates were lower than found in other investigations. Results are discussed to inform investigative procedures, assessment and service response to more adequately respond to children and families when DV is present.
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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 | 0.009 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".