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Record W2946021769 · doi:10.18584/iipj.2019.10.2.5

Community Setting as a Determinant of Health for Indigenous Peoples Living in the Prairie Provinces of Canada: High Rates and Advanced Presentations of Tuberculosis

2019· article· en· W2946021769 on OpenAlexafffundvenueabout
Maria Mayan, Rebecca Gokiert, Ty A. Robinson, Mélissa Tremblay, Sylvia Abonyi, Kirstyn Morley, Richard Long

Bibliographic record

VenueInternational Indigenous Policy Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
FundersCanadian Institutes of Health ResearchHealth Canada
KeywordsIndigenousTuberculosisShameDiseaseMedicineSocioeconomicsGeographyGerontologySociologyPolitical scienceEcologyPathologyBiologyLaw

Abstract

fetched live from OpenAlex

Indigenous Peoples in Canada experience disproportionately high tuberculosis (TB) rates, and those living in the Prairie Provinces have the most advanced TB presentations (Health Canada, 2009). The community settings (i.e., urban centres, non-remote reserves, remote reserves, and isolated reserves) where Indigenous Peoples live can help explain high TB rates. Through qualitative description, we identify how community setting influenced Indigenous people’s experiences by (a) delaying accurate diagnoses; (b) perpetuating shame and stigma; and (c) limiting understanding of the disease. Participants living in urban centres experienced significant difficulties obtaining an accurate diagnosis. Reserve community participants feared being shamed and stigmatized. TB information had little impact on participants’ TB knowledge, regardless of where they lived. Multiple misdiagnoses (primarily among urban centre participants), being shamed for having the disease (primarily reserve community participants), and a lack of understanding of TB can all contribute to advanced presentations and high rates of the disease among Indigenous Peoples of the Prairie Provinces.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.379
Teacher spread0.357 · 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 teacher head, 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

Citations5
Published2019
Admission routes4
Has abstractyes

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