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Record W1936205635 · doi:10.1002/ajhb.22372

Prevalence of obesity among Inuit in Greenland and temporal trend by social position

2013· article· en· W1936205635 on OpenAlexaboutno aff
Peter Bjerregaard, Marit E. Jørgensen

Bibliographic record

VenueAmerican Journal of Human Biology · 2013
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsObesityWaistBody mass indexOverweightDemographyMedicinePublic healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: The purpose of the study was to analyze the temporal trend of obesity among Inuit in Greenland during 1993-2010 according to sex and relative social position. METHODS: Data (N = 5,123) were collected in cross-sectional health surveys among the Inuit in Greenland in 1993-1994, 1999-2001, and 2005-2010. Sociodemographic information was obtained by interview. Information on obesity (body mass index (BMI) and waist circumference) was obtained by clinical examination and in 1993-1994 by interview. Statistics included multiple linear regression and Univariate General Linear Models. RESULTS: Among men the prevalence of overweight (BMI 25-29.9) decreased while general obesity (BMI ≥ 30) did not change. Central obesity increased from 16.0% in 1993-1994 to 25.4% in 2005-2010 (P < 0.001). Among women general and central obesity increased. Central obesity increased from 31.3% in 1993-1994 to 54.2% in 2005-2010 (P < 0.001). In 2005-2010 both general and central obesity showed significantly increasing trends with social position (general obesity: P < 0.001 for men, P = 0.04 for women; central obesity: P < 0.001 for both men and women). The social trend was absent in the earlier surveys. CONCLUSION: General and central obesity is increasing among the Inuit in Greenland. There is an increasing positive association of obesity with social position for both men and women. The high prevalence of obesity is a serious public health problem that is expected to affect the already high prevalence of Type 2 diabetes and its complications.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.012
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.018
GPT teacher head0.344
Teacher spread0.326 · 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

Citations28
Published2013
Admission routes1
Has abstractyes

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