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Record W2946112813 · doi:10.18584/iipj.2019.10.2.4

Social Determinants of Indigenous Health and Indigenous Rights in Policy: A Scoping Review and Analysis of Problem Representation

2019· review· en· W2946112813 on OpenAlexvenueno aff
Emma George, Tamara Mackean, Fran Baum, M. Fisher

Bibliographic record

VenueInternational Indigenous Policy Journal · 2019
Typereview
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
FundersNational Health and Medical Research Council
KeywordsIndigenousIndigenous rightsTokenismSocial determinants of healthSocial policyHealth policyMainstreamPolitical scienceHealth equityEconomic growthPublic policyGovernment (linguistics)Public administrationSociologyHuman rightsHealth careLawEconomics

Abstract

fetched live from OpenAlex

Despite evidence showing the importance of social determinants of Indigenous health and Indigenous rights for health and equity, they are not always recognised within policy. This scoping review identified research on public policy and Indigenous health through a systematic search. Key themes identified included the impact of ongoing colonisation; the central role of government in realising rights; and the difficulties associated with the provision of mainstream services for Indigenous Peoples, including tokenism towards Indigenous issues and the legacy of past policies of assimilation. Our approach to problem representation was guided by Bacchi (2009). Findings from the review show social determinants of Indigenous health and Indigenous rights may be acknowledged in policy rhetoric, but they are not always a priority for action within policy implementation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.067
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0200.024
Science and technology studies0.0020.003
Scholarly communication0.0060.006
Open science0.0020.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.086
GPT teacher head0.499
Teacher spread0.413 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations45
Published2019
Admission routes1
Has abstractyes

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