Characteristics of child welfare investigations reported by healthcare professionals in Ontario: secondary analysis of a regional database
Bibliographic record
Abstract
Objectives This study examines the characteristics and outcomes of child welfare investigations reported by hospital-based and community-based healthcare professionals. Methods A sample of 7590 child maltreatment-related investigations from the Ontario Incidence Study of Reported Child Abuse and Neglect-2018, a cross-sectional study, was analysed. Bivariate analyses compared characteristics of hospital and community healthcare-reported investigations. Chi-square automatic interaction detector analyses were used to predict the most influential factors in the decision to provide a family with services following a child welfare investigation from each referral source. Results Community healthcare-reported investigations were more likely to have a primary concern of physical abuse while hospital-reported investigations were more likely to be focused on assessing risk of future maltreatment. Hospital-reported investigations were more likely to involve noted primary caregiver (eg, mental health issues, alcohol/drug abuse, victim of intimate partner violence (IPV)) and household risk factors. The most significant predictor of service provision following an investigation was having a caregiver who was identified as a victim of IPV in hospital-reported investigations (χ2=30.237, df=1, adj. p<0.001) and having a caregiver for whom few social supports was noted in community healthcare-reported investigations (χ2=18.892, df=1, adj. p<0.001). Conclusion Healthcare professionals likely interact with children who are at high risk for maltreatment. This study’s findings highlight the important role that healthcare professionals play in child maltreatment identification, which may differ across hospital-based and community-based settings and has implications for future collaborations between the healthcare and child welfare systems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".