MétaCan
Menu
Back to cohort
Record W2134590714 · doi:10.18357/ijih42200912322

Health Research, Entitlements and Health Services for First Nations and Métis Women in Manitoba and Saskatchewan

2009· article· en· W2134590714 on OpenAlexaffvenueabout
Margaret Haworth-Brockman, Kathy Bent, Joanne Havelock

Bibliographic record

VenueInternational Journal of Indigenous Health · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of ManitobaPrairie Women's Health Centre of Excellence
Fundersnot available
KeywordsMetisGovernment (linguistics)Health careHealth servicesTreatyPolitical scienceService (business)ConstitutionEconomic growthBusinessMedicineEnvironmental healthLaw

Abstract

fetched live from OpenAlex

Since 1982, the term “Aboriginal” has been defined in the Canadian constitution as including Indian, Inuit and Metis peoples and has become part of the Canadian vocabulary. However, among the groups included in this term, there are significant differences in access to health care services based on treaty and historical entitlements and related government jurisdictions and policies. In spite of good intentions, research on Aboriginal women’s health can fall short when it fails to recognize differences in entitlements and health services available under the term “Aboriginal.” We explored the historical developments leading to current legal entitlements to health care services for First Nations and Metis women. We then interviewed service providers in Manitoba and Saskatchewan to investigate women’s access to health, including barriers created by differing entitlements to services and lack of understanding about services. We discuss why the differences in health service entitlements must be taken into account for health research.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.045
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0100.004
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.402
Teacher spread0.361 · 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 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

Citations7
Published2009
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Indigenous HealthSame topicIndigenous Health, Education, and RightsFrench-language works237,207