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Cognição e ambiente são preditores do desenvolvimento motor de bebês ao longo do tempo

2016· article· pt· W2399088331 on OpenAlexaboutno aff
Keila Ruttnig Guidony Pereira, Raquel Saccani, Nádia Cristina Valentini

Bibliographic record

VenueFisioterapia e Pesquisa · 2016
Typearticle
Languagept
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
FundersUniversidade Federal do Rio Grande do Sul
KeywordsPsychologyHumanitiesArt

Abstract

fetched live from OpenAlex

RESUMO Investigou-se longitudinalmente relações entre desenvolvimento motor e cognitivo, aspectos biológicos, práticas maternas, conhecimento parental e ambiente familiar de bebês. Participaram do estudo 49 bebês (3-16 meses) avaliados com a Alberta Infant Motor Scale e a Escala Mental da Bayley Scale of Infant Development. Os pais responderam o questionário sobre fatores biológicos, Daily Activities of Infant Scale, o Affordances no Ambiente Domiciliar para o Desenvolvimento Motor - Escala Bebê, e o Inventário sobre Conhecimento do Desenvolvimento Infantil. Avaliações foram conduzidas nas escolas ao longo de 4 meses. Foram utilizadas Equações de Estimativa Generalizada, teste de Bonferroni e coeficiente de correlação de Spearman. Observaram-se associações significativas na (1) análise univariada entre desenvolvimento motor e cognitivo e fatores ambientais (escolaridade, renda, disponibilidade de brinquedos, espaço físico, práticas e conhecimento parental, tempo de aleitamento e frequência na escola); (2) multivariada entre o desenvolvimento motor e renda, idade do pai e espaço físico da residência. Concluiu-se que os desenvolvimentos motores e cognitivos se mostraram interdependentes e fatores ambientais se mostraram mais significativos nas associações em detrimento dos biológicos, reforçando-se a importância do lar, do cuidado dos pais e das experiências que a criança vivencia ao longo dos primeiros anos de vida.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

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