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Urban Aboriginal health: Examining inequalities between Aboriginal and non‐Aboriginal populations in Canada

2011· article· en· W1856083884 on OpenAlexafffundvenueabout
Kathi Wilson, Nicolette Cardwell

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Toronto
FundersHealth Canada
KeywordsHealth equityPopulation healthSocial determinants of healthPopulationCommunity healthGeographyInequalityEnvironmental healthSocioeconomicsPublic healthSociologyMedicine

Abstract

fetched live from OpenAlex

This article contributes to the nascent literature on the health of urban Aboriginal people by comparing the health status and determinants of health of the urban Aboriginal and urban non‐Aboriginal population in Canada. Data for the research were taken from the 2001 Aboriginal Peoples Survey (APS) and the 2000–2001 Canadian Community Health Survey (CCHS) Cycle 1.1. Framed within a population health approach, we explore the extent to which health status and determinants of health differ between Aboriginal and non‐Aboriginal populations living in urban areas. Health status is measured by three variables—self‐assessed health status, chronic conditions, and activity limitations. While disparities in health exist between the urban Aboriginal and non‐Aboriginal population, they are not as large as those between the Aboriginal population living on a reserve and non‐Aboriginal people. The social determinants of health are quite similar between the two populations but the results also reveal the significance of cultural factors in shaping health among the urban Aboriginal population. The research demonstrates a need for future research to focus on culturally specific determinants of health as one potential explanation for disparities in health between urban Aboriginal and non‐Aboriginal people.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.005
Science and technology studies0.0070.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.033
GPT teacher head0.283
Teacher spread0.251 · 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.

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

Citations44
Published2011
Admission routes4
Has abstractyes

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