How Can Data Drive Policy and Practice in Child Welfare? Making the Link in Canada
Bibliographic record
Abstract
Formal university-child welfare partnerships offer a unique opportunity to begin to fill the gaps in the child welfare knowledge base and link child welfare services to the realities of practice. With resources from a knowledge mobilization grant, a formal partnership was developed between the University of Toronto, clinicians, policy analysts, and researchers from child welfare agencies across Ontario. The key objectives of the grant included: (1) enhancing the capacity of service providers to access and analyze child welfare data to inform service and policy decisions; (2) integrating clinical expertise in service and policy decisions; and (3) developing a joint research agenda addressing high-priority knowledge gaps. This partnership was an opportunity to advance the evidence base with respect to service provision in Ontario and to create a culture of knowledge and evidence that would eventually support more complex research initiatives. Administrative data was analyzed for this partnership through the Ontario Child Abuse and Neglect Data System (OCANDS)-the first child welfare data system in Ontario to track child welfare-involved children and their families. Child welfare agencies identified recurrence as an important priority and agency-driven analyses were subsequently conducted on OCANDS generated recurrence Service Performance Indicators (SPI's). Using an urgent versus chronic investigative taxonomy for analyses, findings revealed that the majority of cases did not recur within 12 months and cases identified as chronic needs are more likely to return to the attention of child welfare authorities. One of the key outcomes of the partnership - helping agencies to understand their administrative data is described, as are considerations for next steps for future partnerships and research.
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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 | 0.159 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.029 |
| Science and technology studies | 0.024 | 0.022 |
| Scholarly communication | 0.033 | 0.017 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".