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Kindergarten Self‐Regulation As a Predictor of Body Mass Index and Sports Participation in Fourth Grade Students

2012· article· en· W2034287664 on OpenAlexaffabout
Geneviève Piché, Caroline Fitzpatrick, Linda S. Pagani

Bibliographic record

VenueMind Brain and Education · 2012
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversité de MontréalUniversité du Québec en Outaouais
Fundersnot available
KeywordsBody mass indexLongitudinal studyPsychologyPsychological interventionConfoundingDevelopmental psychologyPerspective (graphical)Early childhoodChild developmentClinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Identifying early precursors of body mass index (BMI) and sports participation represents an important concern from a public health perspective and can inform the development of preventive interventions. This article examines whether kindergarten child self‐regulation, as measured by classroom engagement and behavioral regulation, predicts healthy dispositions in fourth grade. To address this objective, secondary analyses were conducted using prospective‐longitudinal data from 966 children followed by the Quebec Longitudinal Study of Child Development. Self‐regulatory skills, including classroom engagement and behavioral regulation, were measured by kindergarten teachers. Greater self‐regulatory skills predicted lower BMI and greater parent‐reported child sports participation, after controlling for a number of potentially confounding child and family characteristics. This article suggests that assessing kindergarten self‐regulatory capacities may help identify children at risk of developing unhealthy dispositions and behaviors in middle childhood.

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 score0.009
Threshold uncertainty score0.349

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 studies0.0000.000
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 head0.013
GPT teacher head0.316
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

Citations21
Published2012
Admission routes2
Has abstractyes

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