Schools Reporting Child Welfare Concerns in Ontario
Bibliographic record
Abstract
Objectives: Currently, there is a dearth of literature surrounding what the profile looks like of a child referred to child welfare services by a school professional. Methods: The Ontario Incidence Study of Reported Child Abuse and Neglect was the first provincial incidence study to track cases of child abuse and neglect (Trocmé, McPhee, & Hay, 1994). The past five cycles (OIS-1993, OIS-1998, OIS-2003, OIS-2008, and OIS-2013), spanning twenty years, offer a unique opportunity for comparisons to be made over time. This study conducted a secondary analysis of the OIS to examine the profile of cases referred by school personnel to child welfare agencies across twenty years. Results: Physical abuse is consistently the most commonly reported type of maltreatment by school professionals. Substantiated investigations resulting from school referrals have remained relatively low across all of the OIS cycles. Discussion: The relatively low percentage of substantiated school referrals across the cycles of the OIS further validate the literature that shows school referrals to be significantly more likely to be unsubstantiated than other professional referrals (King, 2011; King & Scott, 2014; U.S. Department of Health and Human Services, 2007). Substantiation rates have not been above 30% for the past 10 years, and have never been higher than 40%. Further research is needed to offer concrete explanations for this trend.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".