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Record W2062067057 · doi:10.3402/ijch.v69i3.17619

Restoring Aboriginal culture through community-based type 2 diabetes screening

2010· letter· en· W2062067057 on OpenAlexaffabout
Richard T. Oster, Ellen L. Toth

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2010
Typeletter
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsType 2 diabetesAutonomyIndigenousRecreationPsychological interventionPopulationMedicineGerontologyPsychologyDiabetes mellitusEnvironmental healthNursingPolitical science

Abstract

fetched live from OpenAlex

Typically, type 2 diabetes (T2D) exists long before diagnosis, with complications and co-morbidities also beginning years before clinical presentation. So the question arises: Should individuals be screened for T2D? The answer is not clear, and it becomes even more complicated when applied to “vulnerable” populations such as Canadian Aboriginals, who are at an elevated risk of developing T2D (1). In this population, unique cultural and ethical issues arise in addition to the already debated economical, clinical and logistical factors. Does the community have access to affordable healthy foods and recreation infrastructure? Will the discovery of diabetes for some set in motion a negative stigma, such as “living contrary to traditional culture”? Can cultural barriers, for instance language, be an encumbrance to screening? Will screening interventions be viewed as potential threats to Indigenous autonomy? And so on.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0100.011
Insufficient payload (model declined to judge)0.0050.002

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 head0.039
GPT teacher head0.387
Teacher spread0.348 · 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 designObservational
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

Citations1
Published2010
Admission routes2
Has abstractyes

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