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Record W3095250968 · doi:10.32799/ijih.v15i1.33991

Insights from a Jordan’s Principle Child First Initiative in Alberta

2020· article· en· W3095250968 on OpenAlexaffvenueabout
Alison Gerlach, Meghan Sangster, Vandna Sinha

Bibliographic record

VenueInternational Journal of Indigenous Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsMcGill UniversityUniversity of Victoria
Fundersnot available
KeywordsMandateHealth carePolitical sciencePublic administrationEquity (law)Economic growthTransformative learningHuman servicesService (business)Service delivery frameworkPublic relationsSociologyLawBusiness

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.120
GPT teacher head0.431
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes3
Has abstractyes

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