Child Welfare Investigations of Exposure to Intimate Partner Violence Referred by Medical Professionals in Ontario: A Uniquely Vulnerable Population?
Bibliographic record
Abstract
Victims of intimate partner violence (IPV) and their children may be at an increased risk for negative health outcomes and may present to healthcare settings. The objective of the current study is to examine the profile of medical-referred child welfare investigations of exposure to IPV in Ontario, Canada. Data from the Ontario Incidence Study of Reported Child Abuse and Neglect 2018 were used. We compared medical-referred investigations with all other investigations of exposure to IPV. Descriptive and bivariate analyses as well as a logistic regression predicting transfers to ongoing services were conducted. Six percent of investigations of exposure to IPV conducted in Ontario in 2018 were referred by a medical source. Compared to other investigations of exposure to IPV, these investigations were more likely to involve younger children (p = 0.005), caregivers with mental health issues (p < 0.001) and few social supports (p = 0.004), and households noted to be overcrowded (p = 0.001). After controlling for clinical case characteristics, investigations of exposure to IPV referred by healthcare sources were 3.452 times as likely to be kept open for ongoing child welfare services compared to those referred by other sources (95% CI [2.024, 5.886]; p < 0.001). Children and their families who are identified in healthcare settings for concerns of exposure to IPV tend to receive extended child welfare intervention compared to those identified elsewhere. There is a clear difference in service provision in healthcare-originating investigations of exposure to IPV versus investigations originating from other sources. Further research into the services provided to victims of IPV and their children is needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".