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Record W4415323472 · doi:10.1177/11771801251374209

Frameworks and models that promote Métis health: a narrative review

2025· review· en· W4415323472 on OpenAlexafffundabout
José Diego Marques Santos, Tracey Carr, Stacey McHenry, Erin Leeder, Sheila Laroque, Gary Groot

Bibliographic record

VenueAlterNative An International Journal of Indigenous Peoples · 2025
Typereview
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchCanadian Cancer SocietySaskatchewan Health Research FoundationGovernment of Canada
KeywordsIndigenousNarrativeInclusion (mineral)Traditional knowledgeGrey literatureNarrative reviewMEDLINE

Abstract

fetched live from OpenAlex

Culture is a determinant of health for Indigenous peoples, providing a sense of belonging, strengthening resilience, and promoting wellness. Consequently, there is growing interest in exploring the relationship between Métis (distinct Indigenous people, rooted in both First Nations and European ancestry, Canada), culture, and health. A search of scholarly databases and gray literature resulted in 14 records that met inclusion criteria. Articles revealed seven frameworks and three models that applied Métis knowledge to health. Reiterative readings were used to analyze models and frameworks. Results indicated that one or multiple approaches to health were incorporated: (1) Métis symbolism and concepts; (2) social determinants of health and the life course perspective; (3) engagement with Métis government, organizations, and community; and (4) research methods and knowledge translation. These findings can foster the development of culturally appropriate models, frameworks, and strategies that favor Métis culture in health research to more effectively promote Métis health.

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)0.008
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.008
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.002
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 head0.134
GPT teacher head0.534
Teacher spread0.400 · 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 designNot applicable
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

Citations0
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueAlterNative An International Journal of Indigenous PeoplesSame topicPublic Health Policies and EducationFrench-language works237,207