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Record W2772704511 · doi:10.14430/arctic4684

Prevalence and Patterns of Sugar-Sweetened Beverage Consumption in Canadian Youth: A Northern Focus

2017· article· en· W2772704511 on OpenAlexafffundvenueabout
Laura Davis, Colleen Davison

Bibliographic record

VenueARCTIC · 2017
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsQueen's University
FundersCanadian Institutes of Health ResearchHealth CanadaQueen's UniversityPublic Health AgencyPublic Health Agency of Canada
KeywordsConsumption (sociology)Environmental healthGeographySugarDemographyPopulationMedicineFood scienceBiology

Abstract

fetched live from OpenAlex

Regular consumption of sugar-sweetened beverages (SSBs) is a well-known risk factor for weight gain, tooth decay, and metabolic syndrome. Rates of SSB consumption in Nunavut specifically, have been noted to be exceptionally high. This study describes consumption rates of specific foods and beverages, with a focus on SSBs, among adolescents in Nunavut, northern Canada as a whole, and the Canadian provinces, using data from the 2010 and 2014 cycles of the Health Behaviour in School-aged Children (HBSC) study to investigate population characteristics and consumption patterns. Comparative analyses of consumption patterns for Nunavut, the three territories combined, and the southern provinces found that in 2010, those who consumed SSBs once a day or more comprised 53.1% of adolescents in Nunavut, 31.1% in the northern territories and 24% in the provinces. Comparable figures for 2014 were 55.0% in Nunavut, but only 27.0% in all the territories, and 19.1% in the provinces. The percentage of adolescents who consumed fruit and vegetables daily was also lower in Nunavut than in the provinces (65.5% vs. 85.3% in 2010, and 57.5% vs. 84.4% in 2014). More Nunavut adolescents consumed sweets and potato chips daily than provincial adolescents (42.6% vs. 27.6% in 2010, and 52.2% vs. 25.2% in 2014). A greater proportion of Nunavut adolescents reported high consumption of SSBs, as well as other energy-dense foods, when compared to adolescents in the three territories combined and in the provinces. These results confirm previous studies but provide a current and comprehensive analysis that can help inform future food and nutrition priorities and programing.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score1.000

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 studies0.0020.000
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 head0.048
GPT teacher head0.340
Teacher spread0.292 · 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.

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

Citations2
Published2017
Admission routes4
Has abstractyes

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