Trends in investigations of abuse or neglect referred by hospital personnel in Ontario
Bibliographic record
Abstract
BACKGROUND: There is a dearth of literature surrounding mandated reporters to child welfare services in the Canadian context. This paper examines 20 years of reporting patterns from hospitals, which represent 5% of all referrals to child welfare services in Ontario. METHODS: The Ontario Incidence Study of Reported Child Abuse and Neglect (OIS) is a representative study that has taken place every 5 years since 1993. The OIS is a multistage cluster sample design, intended to produce an estimate of reported child abuse and neglect in the year the study takes place. RESULTS: There have been significant changes in referral patterns over time. Hospital referrals in 2013 are more likely to involve a concern of neglect, risk of maltreatment or exposure to intimate partner violence. In 1993, children were more likely to be referred from a hospital for a concern of physical abuse. Between 1993 and 1998, there was a significant drop in the number of sexual abuse investigations referred from a hospital. Hospitals have low rates of substantiation across all of the OIS cycles. CONCLUSION: This is the first study to examine hospital-based referral patterns in Canada. The relatively low percentage of hospital referrals across the cycles of the OIS is consistent with the extant literature. The findings warrant further discussion and research. This study is foundational for future research that can assist in identifying and developing responses across sectors that meet the complex needs of vulnerable families and that ultimately promote children's safety and well-being.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".