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Record W2145664088 · doi:10.1017/s0007114509992224

Assessing dietary intake in a population undergoing a rapid transition in diet and lifestyle: the Arctic Inuit in Nunavut, Canada

2009· article· en· W2145664088 on OpenAlexafffundabout
Sangita Sharma, Xia Cao, Cindy Roache, Annie Buchan, Rhonda Reid, Joel Gittelsohn

Bibliographic record

VenueBritish Journal Of Nutrition · 2009
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsGovernment of Nunavut
FundersHealth CanadaGovernment of NunavutAmerican Diabetes Association
KeywordsEnvironmental healthNutrientPopulationMedicineNutrition transitionArcticAdded sugarFood frequency questionnaireNutrient densityGerontologySugarFood scienceObesityBiologyOverweight

Abstract

fetched live from OpenAlex

The aims of the present study were to (1) characterise the diets of adult Inuit; (2) highlight foods for a nutritional and lifestyle intervention programme; (3) develop a quantitative FFQ (QFFQ) to evaluate the programme and monitor changes in dietary intake in this population over time. A dietary survey using single 24-h dietary recalls was conducted among Inuit aged between 19 and 87 years in two communities in Nunavut, Canada. Eighty-seven subjects completed the recalls (response rate was approximately 73 %). The mean energy intake for men and women was 9530 and 6939 kJ, respectively. The intakes of dietary fibre and the majority of vitamins and minerals (especially vitamins A, D, and E, total folate and Ca) were far below the recommendations. Traditional foods contributed 40 and 42 %, respectively, to protein and Fe intakes. Non-nutrient-dense store-bought foods were consumed much more frequently than the nutrient-dense traditional foods. Foods high in fat and sugar were highlighted, and will be replaced by healthier, more nutrient-dense alternatives to address the dietary inadequacies for the nutritional intervention programme. A 154-item QFFQ was developed and pilot tested in the Arctic Inuit. The present study highlighted foods to be targeted for a nutritional and lifestyle intervention programme not previously undertaken in this population. This QFFQ is culturally appropriate and specific for evaluating the effectiveness of the programme, as well as monitoring nutritional transition in this population.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.025
GPT teacher head0.314
Teacher spread0.289 · 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

Citations98
Published2009
Admission routes3
Has abstractyes

Explore more

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