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The influence of the quality in daycare environments on children’s motor development between six to 15 months old

2021· article· en· W3210436096 on OpenAlexaboutno aff
Keli Lovison, Helenara Salvati Bertolossi Moreira, Joselici da Silva, Luiz Guilherme Scorzafave, Débora Falleiros de Mello

Bibliographic record

VenueRevista Brasileira de Saúde Materno Infantil · 2021
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
Fundersnot available
KeywordsAffordanceMotor skillEarly childhoodPsychologyChild developmentDevelopmental psychologyEarly childhood educationScale (ratio)Typically developingMotor coordinationMotor activityPediatricsMedicineGeographyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Objectives: to analyze the influence of the quality in the environment on children’s motor development between six to 15 months old attending early childhood education centers. Methods: a cross-sectional and analytical study addressing children’s motor development between six to 15 months old, both sexes, enrolled in nursery (0-12 months) and preschoolers (12-24 months). The Affordances in the Home Environment for Motor Development-Infant Scale and Alberta Infant Motor Scale were used in the Brazilian versions Results: the sample was comprised of 104 children and 30 early childhood education centers: presenting a mean age of 9.15 months, 88% attended full-time and 12% part-time schooling (mornings or afternoons). One center was classified with a low Affordances, 26 were considered Sufficient, and three were Adequate. Of the participating children, 40% presented typical motor development. The higher levels of Affordances suggest better motor development, and lower levels of suspected delayed motor development. Conclusion: the environments of early childhood education centers influence children’s motor performance, especially daily activities and the use of toys. The lower the Affordances level, more likely children are suspected of delayed motor development.

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 score0.040
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.276
Teacher spread0.262 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

Explore more

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