MétaCan
Menu
Back to cohort
Record W2074461314 · doi:10.1017/s1368980009990711

Are snacking patterns associated with risk of overweight among Kahnawake schoolchildren?

2009· article· en· W2074461314 on OpenAlexafffund
Geneviève Mercille, Olivier Receveur, Ann C. Macaulay

Bibliographic record

VenuePublic Health Nutrition · 2009
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsMcGill UniversityUniversité de Montréal
FundersCanadian Institutes of Health ResearchHealth Canada
KeywordsOverweightSnackingMedicineEnvironmental healthLogistic regressionDemographyBody mass indexObesityEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To understand more specifically how the quality, quantity and frequency of snack food consumption differs in different BMI categories. DESIGN: Four hundred and forty-nine school-aged children (grade 4-6) from a Kanien'kehaka (Mohawk) community provided a 24 h recall and their height and weight in 1994, 1998 and 2002, in three independent cross-sectional samples. Food consumed between two consecutive meals was defined as a snacking occasion. ANOVA and chi2 tests were used to compare food choices between BMI categories according to food quality criteria and food groups in 2006. Logistic regression models were performed to compare results between normal-weight children and those at risk of overweight and between normal-weight and overweight children. RESULTS: Energy intake from snacks tended to be higher for children at risk of overweight, compared with the other two BMI categories. Food groups with a higher energy density were also consumed more frequently by these children, with larger average portions of cereal bars (P < 0.05). Except for dessert consumption, which was less frequent among overweight children, no other variable distinguished risk of overweight in the two logistic regression models tested. CONCLUSIONS: Differences detected in snack food intake between normal-weight children and children at risk of overweight could explain in part the relationship between food choices and risk of overweight. Studies of dietary differences in conjunction with body weight would benefit from considering children at risk of overweight and normal-weight children, rather than children with excess weight only.

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 score0.011
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.279
Teacher spread0.256 · 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

Citations27
Published2009
Admission routes2
Has abstractyes

Explore more

Same venuePublic Health NutritionSame topicObesity, Physical Activity, DietFrench-language works237,207