Child welfare investigations involving exposure to intimate partner violence: Case and worker characteristics
Bibliographic record
Abstract
Objectives: This paper explores child welfare investigations involving three forms of children’s exposure to intimate partner violence (IPV): direct witness to physical violence, indirect exposure to physical violence, and exposure to emotional violence, and the characteristics associated with these subtypes. These data allow the exposure to IPV typology to be more precisely examined as the subtypes define the specific event(s) investigated. Methods: Using a large representative dataset of an estimated 22,373 investigations, clinical and case characteristics are examined. Bivariate analyses are conducted in order to assess differences for the three forms of IPV. Results: Investigations involving children’s direct witnessing of physical violence was most frequently substantiated and kept open for ongoing child welfare services compared to other forms of exposure. Caregiver risk factors differed significantly between the three subtypes of exposure to IPV. Some worker characteristics were also significantly different (e.g., social work degree, and domestic violence training) depending on the type of exposure IPV being investigated. Conclusions and Implications: These results have important policy and practice implications in that they show that a differential systems response is needed for exposure to IPV, depending on the type of exposure and the child, family, and household risk factors present. The results also suggest that some workers may require additional domestic violence training.
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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 | 0.004 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".