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Record W4386885760 · doi:10.32920/24168459.v1

Nutritional Risk in Early Childhood and School Readiness

2023· preprint· en· W4386885760 on OpenAlexafffundabout
Jessica Omand, Magdalena Janus, Jonathon L. Maguire, Patricia C. Parkin, Mary Aglipay, Janis Randall Simpson, Charles Keown‐Stoneman, Eric Duku, Caroline Reid‐Westoby, Catherine S. Birken

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of TorontoSickKids FoundationPublic Health OntarioUniversity of GuelphHospital for Sick ChildrenSt. Michael's HospitalMcMaster University
FundersInstitute of Nutrition, Metabolism and DiabetesInstitute of Human Development, Child and Youth HealthDanone Institute of CanadaInstituto DanoneCouncil for Science and Technology PolicyHospital for Sick ChildrenCanadian Child Health Clinician Scientist ProgramDairy Farmers of OntarioSt. Michael's Hospital FoundationCanadian Institutes of Health ResearchDanoneCentre for Addiction and Mental Health FoundationDairy Farmers of CanadaMead Johnson Nutrition
KeywordsMilestoneMedicineProspective cohort studyPediatricsCohortFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Background Nutrition in early childhood is important for healthy growth and development. Achieving school readiness is considered one of the most important developmental milestones for young children. Objectives The purpose of this study is to determine if nutritional risk in early childhood is associated with school readiness in kindergarten. Methods A prospective cohort study was conducted through The Applied Research Group for Kids (TARGet Kids!) primary care research network in Toronto, Canada, 2015–2020. Nutritional risk was measured (18 mo to 5 y) using validated parent-completed questionnaires called Nutrition Screening for Toddlers and Preschoolers (NutriSTEP). High nutritional risk was categorized as scores ≥21. School readiness was measured using the validated teacher-completed Early Developmental Instrument (EDI), which measures 5 developmental domains in kindergarten (2 y of schooling, ages 4–6 y, before they enter grade 1). Vulnerability indicates scores lower than a population-based cutoff at the 10th percentile on at least 1 domain. Multiple logistic and linear regression models were conducted adjusting for relevant confounders. Results The study included 896 children: 53% were male, 9% had high nutritional risk, and 17% were vulnerable on the EDI. A 1-SD increase in NutriSTEP total score was associated with 1.54 times increased odds of being vulnerable on the EDI among children in year 2 of kindergarten (P = 0.001). High nutritional risk cutoff was associated with 4.28 times increased odds of being vulnerable on the EDI among children in year 2 of kindergarten (P < 0.001). NutriSTEP total score and high nutritional risk were associated with lower scores on all 5 EDI domains, with the strongest association observed for the domains of language and cognitive development and communication skills and general knowledge. Conclusions Higher nutritional risk in early childhood is associated with lower school readiness in year 2 of kindergarten. Nutritional interventions early in life may offer opportunities to enhance school readiness. This trial was registered http://www.clinicaltrials.gov as NCT01869530.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.020
GPT teacher head0.272
Teacher spread0.253 · 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

Citations0
Published2023
Admission routes3
Has abstractyes

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