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Record W1974698749 · doi:10.3402/ijch.v64i2.17966

Food use of Dene/Métis and Yukon children

2005· article· en· W1974698749 on OpenAlexaffabout
Tomoko Nakano, Karen Fediuk, Norma Kassi, Harriet V. Kuhnlein

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2005
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMcGill University
Fundersnot available
KeywordsRiboflavinNutrientFood groupVitaminFood scienceEnvironmental healthArcticMedicineBiologyEcologyBiochemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe food use of Dene/Métis and Yukon children with focus on food sources--traditional food (TF) and market food (MF), season, gender and location. STUDY DESIGN: Children of 10-12 years of age were interviewed for 24-h recalls (n = 222 interviews) in five communities during two seasons in 2000-2001. METHODS: Differences in children's food and nutrient intakes when consuming or not consuming at least one item of TF and across three regions were tested using ANCOVA after rank transformation of raw values. Food use was described and compared by food groups. RESULTS: MF was the major portion of the diet, with TF contributing only an average 4.3%-4.7% of energy in the two seasons. Most TF was in the form of land animal meats. More than half of the energy intake from MF came from less nutrient dense food items. In spite of low TF intake, children who consumed TF had significantly (P < or = 0.05) more protein, iron, zinc, copper, magnesium, phosphorus, potassium, vitamin E, riboflavin and vitamin B6 than those who did not. Children in the more northern communities consumed significantly (P < or = 0.05) more TF, protein, iron, copper, vitamin B6 and manganese, and less energy, fat, saturated fat and sodium. CONCLUSIONS: Extensive use of less nutrient-dense food by children is a concern, suggesting a need for dietary improvement. Use of more TF should be encouraged, especially for children living in more southern Arctic communities near commercial centers.

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 score0.103
Threshold uncertainty score0.430

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 integrity0.0000.000
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 head0.046
GPT teacher head0.391
Teacher spread0.344 · 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

Citations46
Published2005
Admission routes2
Has abstractyes

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