Motor development of children between 0 and 18 months of age : differences between sexes
Bibliographic record
Abstract
Os primeiros anos na vida da criança são fundamentais para a aquisição de habilidades motoras, cognitivas e sociais; desta forma as oportunidades ofertadas as crianças e as expectativas sociais podem interferir no desenvolvimento. O objetivo deste estudo foi avaliar e comparar o desenvolvimento motor de meninos e meninas na primeira infância. Participaram deste estudo, 90 crianças de 0 a 18 meses (45 meninas e 45 meninos pareados por idade), residentes no sul do Brasil, provenientes de Escolas de Educação Infantil. A Alberta Infant Motor Scale (AIMS) foi utilizada para avaliar o desempenho motor. A maioria dos participantes (73.3%) apresentou desempenho motor normal; não foram observadas diferenças significativas no desenvolvimento motor entre os sexos nos escores da AIMS (pesc.total = .76; ppercentil = .38) e nas diferentes posturas (pprono = .71; psupino = .49; psentado = .71; pempé = .97;, não foram observadas associações entre sexo e desempenho motor (Eta² = .008; Eta² = .108). Nos primeiros dois anos de vida, meninos e meninas demonstram desempenho motor amplo similar. Infere-se que as diferenças entre sexo que aparecem com o passar do tempo são influenciadas pelas oportunidades do contexto.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".