Examining the Impact of Policy and Legislation on the Identification of Neglect in Ontario: Trends Over-Time
Bibliographic record
Abstract
Objectives: Reported neglect investigations were compared across a 20-year time \nframe using data from the five cycles of the Ontario Incidence Study of Reported Child \nAbuse and Neglect (OIS-1993 to 2013) in order to discuss the impact of significant policy \nchanges on the Ontario child welfare system’s response to child neglect. \nMethods: Each OIS cycle used a multi-stage sampling design. A representative sample \nwas selected from all mandated child welfare organizations. Cases were selected over a \nthree-month period and then weighted to produce provincial estimates. The information \nwas collected directly from child welfare workers at the conclusion of the investigation \nusing a three-page data collection instrument. \nResults: Changes in rates of reported neglect vary by form but overall there has been \na significant increase in reported neglect in Ontario since 1993. There was a decline in \ninvestigations involving permitting criminal behaviour, which was the most investigated \nform of neglect in 1993 and least investigated in 2013. Physical and medical neglect \nincreased dramatically between 1998 and 2003. Transfers to ongoing services for neglect \ninvestigations remained relatively stable despite the doubling of neglect investigations. \nConclusion and Implications: Transfer to ongoing services did not increase consistently \nwith the investigation rate. This could be reflecting a significant resource gap, whereby \nthe number of children and families receiving ongoing child welfare services is \ndetermined by capacity rather than need or it could mean that referral processes are mistakenly identifying situations that do not need child welfare services. Further analysis \nis required to understand these trends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".