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Record W4310937671 · doi:10.17269/s41997-022-00724-7

Sociodemographic patterning of dietary profiles among Inuit youth and adults in Nunavik, Canada: a cross-sectional study

2022· article· en· W4310937671 on OpenAlexafffundvenueabout
Amira Aker, Pierre Ayotte, Chris Furgal, Tiff‐Annie Kenny, Matthew Little, Marie-Josée Gauthier, Amélie Bouchard, Mélanie Lemire

Bibliographic record

VenueCanadian Journal of Public Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsNunavik Regional Board of Health and Social ServicesThe Quebec Population Health Research NetworkUniversity of VictoriaTrent UniversityInstitut National de Santé Publique du QuébecUniversité Laval
FundersNorthern Contaminants ProgramSentinelle Nord, Université LavalFonds de Recherche du Québec - SantéMinistère de la SantéMinistère de la Santé et des Services sociauxArcticNetInstitut National de Santé Publique du QuébecCrown-Indigenous Relations and Northern Affairs CanadaUniversité Laval
KeywordsEnvironmental healthMultinomial logistic regressionConsumption (sociology)Cross-sectional studyGeographyLogistic regressionFood consumptionDemographyMedicineEconomics

Abstract

fetched live from OpenAlex

OBJECTIVES: Country (traditional) foods are integral to Inuit culture, but market food consumption is increasing. The Qanuilirpitaa? 2017 Nunavik Health Survey (Q2017) reported similar country food consumption frequency compared to that in 2004; however, examining food items individually does not account for diet patterns, food accessibility, and correlations between food items. Our objective was to identify underlying dietary profiles and compare them across sex, age, ecological region, and food insecurity markers, given the links among diet, health, and sociocultural determinants. METHODS: Food frequency and sociodemographic data were derived from the Q2017 survey (N = 1176). Latent profile analysis identified dietary profiles using variables for the relative frequencies of country and market food consumption first, followed by an analysis with those for country food variables only. Multinomial logistic regression examined the associations among dietary profiles, sociodemographic factors, and food insecurity markers (to disassociate between food preferences and food access). RESULTS: Four overall dietary profiles and four country food dietary profiles were identified characterized by the relative frequency of country and market food in the diet. The patterns were stable across several sensitivity analyses and in line with our Inuit partners' local knowledge. For the overall profiles, women and adults aged 30-49 years were more likely to have a market food-dominant profile, whereas men and individuals aged 16-29 and 50+ years more often consumed a country food-dominant profile. In the country food profiles, Inuit aged 16-29 years were more likely to have a moderate country food profile whereas Inuit aged 50+ were more likely to have a high country food-consumption profile. A low country and market food-consumption profile was linked to higher prevalence of food insecurity markers. CONCLUSION: We were able to identify distinct dietary profiles with strong social patterning. The profiles elucidated in this study are aligned with the impact of colonial influence on diet and subsequent country food promotion programs for Inuit youth. These profiles will be used for further study of nutritional status, contaminant exposure, and health to provide context for future public health programs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.071
GPT teacher head0.341
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2022
Admission routes4
Has abstractyes

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