Children's Exposure to Intimate Partner Violence (IPV): Challenging Assumptions about Child Protection Practices
Bibliographic record
Abstract
Objective: While child welfare policy and legislation in Canada and other regions has increasingly defined children who are exposed to intimate partner violence (IPV) as maltreated, little is known about the response of the child welfare system to children exposed to IPV. This dissertation seeks to determine the response of child protection services to reports about IPV; the outcomes of child protection investigations referred for children exposed to IPV; and what factors predict decisions made by child protection workers (e.g., court involvement, child welfare placement, closing the case, substantiation, and making a referral to other supportive services) for investigations involving children exposed to IPV. 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 and multiple multinomial regression were conducted to study the substantiation of maltreatment, and logistic regression was conducted to predict child protection workers’ decisions to keep the case open, make a child welfare placement, take the family to court, and make a referral for investigations involving children’s exposure to IPV. Results: Seventy-eight percent of the investigations involving only IPV were substantiated. The multinomial regression could not accurately classify the suspected and unfounded cases. Substantiated investigations involving co-occurring IPV were three times more likely to stay open for ongoing child protection services compared to IPV only cases (adjusted odds ratio = 2.93, p < .001). Child protection service outcomes for substantiated investigations involving co-occurring IPV were ten times more likely (adjusted odds ratio = 10.61, p < .001) to involve a child welfare placement compared to IPV only cases. The factors driving child protection worker decisions were primarily the co-occurrence of maltreatment, younger children, and emotional harm. Conclusions: Due to the expansion in what constitutes a child in need of protection, there has been an overwhelming increase in the number of referrals about children being exposed to IPV in Canada. Future analyses should explore whether policies that direct IPV cases to child welfare services is both an effective way of protecting children and a reasonable use of limited child welfare resources.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".