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Record W2086439189 · doi:10.3402/ijch.v69i2.17435

The relationship between socio-economic and geographic factors and asthma among Canada’s Aboriginal populations

2010· article· en· W2086439189 on OpenAlexafffundabout
Eric Crighton, Kathi Wilson, Sacha Senécal

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsWestern UniversityAboriginal Affairs Northern Dev CanadaUniversity of TorontoUniversity of Ottawa
FundersIndigenous and Northern Affairs CanadaUniversity of Ottawa
KeywordsAsthmaResidenceGeographyLogistic regressionDemographyLocationSocioeconomic statusMedicinePopulationMultivariate analysisRural areaEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine the prevalence, exacerbations and management of asthma among Canada's Aboriginal populations, and its relationship to socio-economic and geographic factors. STUDY DESIGN: Secondary analysis of a national cross-sectional questionnaire survey. METHODS: Data were collected in 2000 and 2001 through a survey of Aboriginal children and adults residing on- and off-reserve as part of the 2001 Aboriginal People's Survey (APS). The asthma related outcome variables - physician-diagnosed asthma, attack in past year and regular use of inhalants - were examined in relation to socio-economic and geographic factors such as income, education, housing and location of residence. Statistical analyses were based on weighted univariate and multivariate logistic regressions. RESULTS: The results show variations in asthma diagnosis, attacks and inhalant use across geographic location, socio-economic and demographic characteristics. Geographic location was found to be significantly associated with asthma for both adults and children, with those living in the northern territories, on-reserve or rural locations being the least likely to be diagnosed. Geographic location and Aboriginal identity were also found to be significantly associated with asthma medication use. CONCLUSIONS: While these findings may suggest a "healthier" population in more remote locations, they alternatively point to a general pattern of under-diagnosis, potentially due to poor health care access, as is typical in more remote locations.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience 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 score0.204
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.345
Teacher spread0.323 · 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

Citations45
Published2010
Admission routes3
Has abstractyes

Explore more

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