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Record W3080890778 · doi:10.1111/cag.12651

Revealing circumstances of epidemiologic transition among Indigenous peoples: The case of the Keg River (Alberta) Métis

2020· article· en· W3080890778 on OpenAlexaffvenueabout
P. A. Hackett, Sylvia Abonyi, Rachel Engler‐Stringer

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2020
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of SaskatchewanSaskatchewan Health
Fundersnot available
KeywordsIndigenousObesityDiseaseType 2 diabetesGeographyGovernment (linguistics)Epidemiological transitionGestational diabetesEnvironmental healthMedicineDiabetes mellitusDevelopment economicsGerontologyPregnancyEcologyEconomicsBiology

Abstract

fetched live from OpenAlex

Rates of type 2 diabetes and other metabolic disorders are elevated among Indigenous peoples; however, no research has examined the origins of these diseases among the Métis. This case study documents a transition in lifestyle and health that affected the Keg River Métis of northern Alberta during the middle decades of the 20thcentury. This community began to experience previously absent diseases, including obesity, heart disease, gestational and type 2 diabetes, and preeclampsia. This shift in disease burden appears tied to rapid socio‐cultural and economic change driven by a decline of traditional economic activities, access to government transfer payments and wage labour, an increasingly sedentary lifestyle, and a growing availability of non‐traditional foods. This study points to earlier emergence of diabetes among Canadian Indigenous populations than commonly credited and presents the case for a rapidly evolving epidemic tied to environmental and cultural change. Underlying this were structural changes that emerged out of colonization.

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)0.001
metaresearch head score (Gemma)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0130.005
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
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 head0.021
GPT teacher head0.260
Teacher spread0.239 · 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

Citations3
Published2020
Admission routes3
Has abstractyes

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