National Child Welfare Outcomes Indicator Matrix (NOM)
Bibliographic record
Abstract
Over 200,000 children and youth1 come into contact with child welfare authorities every year across Canada (Trocmé, Fallon, et al. 2005) and on any one day of the year over 67,000 children and youth are living in out-of-home care (Mulcahy &Trocmé, 2009). While this is one of the most high risk groups of children in Canada, there is currently no common framework for tracking how well children receiving child welfare services are doing.The purpose of this document is to present a common set of indicators developed to track outcomes for these children. A Multi-Dimensional Outcomes Framework The National ChildWelfare Outcomes Indicator Matrix (NOM) was developed through a series of consultations ini-tiated by the provincial and territorial Directors of Child Welfare and Human Resources Development Canada (Trocmé,Nutter,MacLaurin, & Fallon, 1999). It provides a framework for tracking outcomes for children and families receiving child welfare services that can be used as a common set of indicators across jurisdictions.The NOM is designed to reflect the complex balance that child welfare authorities maintain between a child’s immediate need for protection; a child’s long-term requirement for a nurturing and stable home; a family’s potential for growth, and; the community’s capacity to meet a child’s needs.The NOM includes four nested domains: child safety, child well-being, permanence, and family and community support (see NOM ecological framework). The final ten NOM indicators were selected on the basis of information that
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".