Effects of Individual and School-Level Characteristics on a Child’s Gross Motor Coordination Development
Bibliographic record
Abstract
The aim of this study was to identify child and school-level characteristics that explained inter-individual differences in gross motor coordination (GMC). Participants (n = 390), recruited from 18 Portuguese primary schools, were aged 6 to 10 years of age. Birth weight, body fat (BF), physical activity (PA), physical fitness (PF) and GMC were assessed. School size, setting, infrastructure and physical education classes were considered as school context markers. A multilevel modeling approach was used to identify hierarchical effects (child and school levels). It was found that children-level variables (sex, PF, and BF) significantly explained 63% of the 90% variance fraction at the individual level; boys outperformed girls (p < 0.05), individuals with higher BF were less coordinated (p < 0.05), and those with higher PF were more coordinated (p < 0.05). School-variables (e.g. school size and playing surface) explained 84% of the 10% variation fraction. These findings confirm the roles of sex, PFS and BF. Interestingly they also suggest that the school environment plays a minor but significant role in GMC development. However, it is important to stress that the school context and conditions can also play an important role in a child's motor development, providing adequate and enriching motor opportunities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".