Child Protective Service Referrals Involving Exposure to Domestic Violence: Prevalence, Associated Maltreatment Types, and Likelihood of Formal Case Openings
Bibliographic record
Abstract
Childhood exposure to domestic violence (CEDV) is widely understood as potentially harmful to children. Accordingly, many child welfare systems in the United States construe CEDV as maltreatment when the exposure results in or threatened to the child. The purpose of the current study was to investigate substantiated child welfare referrals directly related to CEDV to better understand the prevalence and patterns of CEDV-related maltreatment and how child welfare workers respond under the or threatened harm standard. Data were drawn from 23,704 substantiated referrals between 2009 and 2013 in a large Midwestern child welfare system. Approximately 20% of substantiated referrals were CEDV related. A plurality of CEDV-related referrals included both a male caregiver and female caregiver who were co-substantiated for maltreatment. The most common maltreatment types substantiated for these referrals were neglect based rather than abuse based, and just under a quarter (23%) of CEDV-related referrals were formally opened for services. Referrals involving co-occurring substance abuse were most likely to be opened for services based on predicted probabilities derived from multilevel modeling. Implications for policy and practice are considered.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".