Insights from a Jordan’s Principle Child First Initiative in Alberta
Bibliographic record
Abstract
In 2016 Canada was ordered to implement Jordan’s Principle by the Canadian Human Rights Tribunal. In response to the order Canada created the Child First Initiative to provide federal funding for provincial and territorial organizations supporting First Nation’s children’s health, education, and social service needs, including service coordination. In the shifting national landscape of Child First Initiative funding, there is a lack of evidence on how pediatric healthcare services are addressing the serious health and healthcare inequities experienced by many First Nations children. This paper describes the implementation of a Child First Initiative by the First Nations Health Consortium in the Alberta region, and research findings that provide insights into the complexity and challenges of advancing First Nations children’s health and health equity within the current federal Child First Initiative mandate in this province. This paper highlights the need for transformative pediatric healthcare approaches that expand beyond an individual and demand-driven system and orient towards practices and policies that are socially-responsive. Also, that First Nations leaders and Jordan’s Principle initiatives play a leading role in the design and delivery of all pediatric healthcare services with First Nation communities, families and children across Canada.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.010 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".