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Record W1571595592 · doi:10.18584/iipj.2012.3.1.2

Regionalization as an Opportunity for Meaningful Indigenous Participation in Healthcare: Comparing Canada and New Zealand

2012· article· en· W1571595592 on OpenAlexafffundvenueabout
Josée G. Lavoie, Amohia Boulton, Laverne Gervais

Bibliographic record

VenueInternational Indigenous Policy Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of ManitobaUniversity of Northern British Columbia
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsLegislationIndigenousClosing (real estate)Health policyHealth carePolitical scienceEconomic growthPublic relationsPublic administrationCommunity healthBusinessLaw

Abstract

fetched live from OpenAlex

In Canada and New Zealand, policies support Indigenous participation in the planning and delivery of community-based primary health services. However, these services represent only a fraction of the health services accessed by Indigenous peoples. In New Zealand, legislation enacted in 2000 introduced mechanisms to ensure that Māori have a voice in the decisions made by health boards. In Canada, neither policies nor legislation currently ensure that Aboriginal communities are represented in provincial health systems or regional health boards. The New Zealand experience shows that adding mechanisms of participation to legislation and policies creates opportunities for Māori and health boards to engage in discussions about how to best allocate resources to reduce disparities between Māori and non-Māori health outcomes. In Canada, this dialogue may not occur. Requiring that such mechanisms be created in all Canadian jurisdictions would establish meeting places for dialogue, and assist in closing policy and access gaps that remain.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.082
GPT teacher head0.406
Teacher spread0.324 · 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.

Study designNot applicable
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

Citations6
Published2012
Admission routes4
Has abstractyes

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