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Record W2003919166 · doi:10.3402/ijch.v62i0.18212

Inuit health in Greenland: a population survey of life style and disease in Greenland and among Inuit living in Denmark

2003· article· en· W2003919166 on OpenAlexaboutno aff
Peter Bjerregaard, Tim Curtis, K. Borch‐Johnsen, Gert Mulvad, Ulrik Becker, Stig Andersen, Vibeke Backer

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2003
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePopulationAnthropometryIndigenousDemographyData collectionGeographyEnvironmental healthEcologyInternal medicineBiology

Abstract

fetched live from OpenAlex

During 1997-2001 a population survey was carried out amongst Greenland Inuit living in Denmark and West Greenland (Nuuk, Sisimiut, Qasigiannguit and four villages in Uummannaq municipality). Data collection comprised an interview, a questionnaire, clinical examinations and sampling of biological specimens (blood, urine, subcutaneous fat tissue). The clinical examinations included anthropometric measurements, an oral glucose tolerance test, ECG, ultrasound of thyroid gland and carotid arteries, a skin prick test, and lung function. The data collection areas in Greenland ranged from the westernized capital of Nuuk (pop. app. 13,000) to small fishing and hunting villages (pop. app. 250). A total of 4,162 persons aged 18+ participated in the study; clinical examinations were performed on 2,056 of these, 739 from Denmark and 1317 from Greenland. Some of the above mentioned procedures were performed on a subset of the participants. The participation rate was 62%. We provide an overview of the background of the study and a detailed description of the methods employed for the data collection. A set of standard tables are provided for the indigenous population of Greenland. These cover statistics for selected variables by gender and ten-year age groups.

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)0.012
metaresearch head score (Gemma)0.004
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 score0.177
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.038
GPT teacher head0.398
Teacher spread0.359 · 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

Citations95
Published2003
Admission routes1
Has abstractyes

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