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Record W1493028989

Physical activity for preschool children--how much and how?

2007· article· en· W1493028989 on OpenAlexaff
Brian W. Timmons, Patti‐Jean Naylor, Karin A. Pfeiffer

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsMcMaster UniversityMcMaster Children's Hospital
Fundersnot available
KeywordsDevelopmental psychologyPhysical activityPsychosocialEarly childhoodPsychologyChildhood obesityChild developmentMotor skillCognitionErikson's stages of psychosocial developmentObesityMedicineOverweightPsychiatryPhysical therapy
DOInot available

Abstract

fetched live from OpenAlex

Alarming trends in childhood obesity even among preschool children have re-focused attention on the importance of physical activity in this age group. With this increased attention comes the need to identify the amount and type of physical activity appropriate for optimal development of preschool children. The purpose of this paper is to provide the scientific evidence to support a link between physical activity and biological and psychosocial development during early childhood (ages 2-5 years). To do so, we summarize pertinent literature informing the nature of the physical activity required to promote healthy physical, cognitive, emotional, and social development during these early years. A particular focus is on the interaction between physical activity and motor skill acquisition. Special emphasis is also placed on the nature of physical activity that promotes healthy weight gain during this period of childhood. The paper also discusses the strongest determinants of physical activity in preschool-age children, including the role of the child's environment (e.g., family, child-care, and socio-economic status). We provide recommendations for physical activity based on the best available evidence, and identify future research needs.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.260
Teacher spread0.241 · 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

Citations275
Published2007
Admission routes1
Has abstractyes

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