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Record W1982494860 · doi:10.3390/ijerph9041326

Physical Activity Promotion in the Preschool Years: A Critical Period to Intervene

2012· review· en· W1982494860 on OpenAlexaff
Gary S. Goldfield, Alysha L. J. Dingwall‐Harvey, Kimberly P. Grattan, Kristi B. Adamo

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2012
Typereview
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsCarleton UniversityChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsIntervention (counseling)Health promotionPromotion (chess)Physical activityPopulationPsychologyIntervention mappingObesityMedicineDevelopmental psychologyGerontologyPhysical therapyPublic healthNursingEnvironmental health

Abstract

fetched live from OpenAlex

The primary aim of this paper is to provide a rationale for the necessity of intervening with a physical activity intervention in the preschool years and why the daycare environment is amenable to such intervention. We also review the prevalence of physical activity, sedentary behaviour and obesity in the preschool population and the impact that these lifestyle behaviours and conditions have on the health of preschool aged children, as secondary objectives. Moreover we discuss implications for intervention and research using a "lessons learned" model based on our research team's experience of conducting a randomized controlled trial aimed at increasing physical activity, reducing sedentary behaviour and improving motor skill development and body composition in preschoolers. Lastly, we make conclusions based on the literature and highlight issues and directions that need to be addressed in future research in order to maximize health promotion and chronic disease prevention in the pediatric population.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.185
GPT teacher head0.495
Teacher spread0.310 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations300
Published2012
Admission routes1
Has abstractyes

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