The Canadian child welfare system response to exposure to domestic violence investigations
Bibliographic record
Abstract
Objective: While child welfare policy and legislation reflects that children who are exposed to domestic violence are in need of protection because they are at risk of emotional and physical harm, little is known about the profile of families and children identified to the child welfare system and the system’s response. The objective of this study was to examine the child welfare system’s response to child maltreatment investigations substantiated for exposure to domestic violence (EDV). Methods: This study is based on a secondary analysis of data collected in the 2003 Canadian Incidence Study of Reported Child Abuse and Neglect (CIS-2003). Bivariate analyses were conducted on substantiated investigations. A binary logistic regression was also conducted to attempt to predict child welfare placements for investigations involving EDV. Results: What emerges from this study is that the child welfare system’s response to EDV largely depends on whether it occurs in isolation or with another substantiated form of child maltreatment. For example, children involved in substantiated investigations that involve EDV with another form of substantiated maltreatment are almost four times more likely than investigations involving only EDV to be placed in a child welfare setting (Adjusted Odds Ratio = 3.87, p <.001).
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".