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Record W2965398237 · doi:10.3390/nu11081744

Food Insecurity and Nutritional Risk among Canadian Newcomer Children in Saskatchewan

2019· article· en· W2965398237 on OpenAlexaffabout
Ginny Lane, Christine Nisbet, Hassan Vatanparast

Bibliographic record

VenueNutrients · 2019
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsFood insecurityEnvironmental healthFood securityPsychologyMedicineBusinessGeographyAgriculture

Abstract

fetched live from OpenAlex

Food insecurity continues to persist among vulnerable groups in Canada, including newcomer families. This mixed-methods study uses an exploratory sequential design to characterize the food security status of newcomer families with children aged 3–13 years. Parents completed food security and 24-hour dietary recall questionnaires, and parents and service providers were interviewed to explore their food insecurity experiences. Fifty percent of participant households experienced food insecurity, while 41% of children were food insecure. More recent newcomer families, and families with parents that had completed high school or some years of postsecondary training, more commonly experienced household food insecurity, compared to families with parents without high school diplomas or those with university degrees. Food-insecure children aged 4–8 years were at higher risk of consuming a lower proportion of energy from protein, lower servings of milk products, and inadequate intakes of vitamin B12 and calcium. Participants identified changes in food buying habits due to low income, using food budgets to purchase prescription drugs and to repay transportation loans, while the school food environment impacted children’s food security. Food security initiatives targeting newcomers may benefit from building on the strengths of newcomers, including traditional dietary practices and willingness to engage in capacity-building programming.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.858

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.348
Teacher spread0.303 · 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

Citations40
Published2019
Admission routes2
Has abstractyes

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