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Record W2076888723 · doi:10.3402/ijch.v71i0.18419

Association between socioeconomic status and overweight and obesity among Inuit adults: International Polar Year Inuit Health Survey, 2007–2008

2012· article· en· W2076888723 on OpenAlexaffabout
Natalia Zienczuk, GraceM. Egeland

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2012
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMcGill University
Fundersnot available
KeywordsOverweightSocioeconomic statusObesityDemographyEnvironmental healthBody mass indexAssociation (psychology)MedicineGerontologyGeographyPsychologySociologyPopulation

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate the socio-economic correlates of overweight and obesity among Inuit undergoing rapid cultural changes. STUDY DESIGN: A cross-sectional health survey of 2,592 Inuit adults from 36 communities in the Canadian Arctic. METHODS: Main outcome measures were overweight and obesity (BMI>25 kg/m2 and >30 kg/m2, respectively) and as characteristics were similar, groups were combined into an at-risk BMI category (BMI>25 kg/m2). Logistic regression was used to determine the association between various sociodemographic characteristics and physical activity with overweight and obesity. RESULTS: The prevalence of overweight and obesity was 28 and 36%, respectively, with a total prevalence of overweight and obesity of 64%. In analyses of sociodemographic variables adjusted for age, gender and region, higher education, any employment, personal income, and private housing were all significantly positively correlated with an at-risk BMI (p≤0.001). Smoking, Inuit language as primary language spoken at home, and walking were inversely associated with overweight and obesity. CONCLUSIONS: The current findings highlight the social disparities in overweight and obesity prevalence in an ethnically distinct population undergoing rapid cultural changes.

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)0.007
metaresearch head score (Gemma)0.001
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 score0.047
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.350
Teacher spread0.329 · 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

Citations40
Published2012
Admission routes2
Has abstractyes

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