Individual, school and natural environment influences on children with motor coordination problems: The Peruvian Health and Optimist Growth Study
Bibliographic record
Abstract
Abstract This study investigates the interplay between individual and natural environments, as well as the effects of school characteristics, on Peruvian children and adolescents' gross motor coordination (GMC) problems. The sample comprises 7401 participants (4121 girls) aged 6–14 years from three geographical regions: sea-level, Amazon and high-altitude. GMC categories (normal and with problems) were defined from the KörperkoordinationsTest für Kinder test battery. Stunting (height-for-age) and nutritional status (BMI-for-age) were obtained from WHO Growth Standards. Biological maturation was estimated, and physical fitness was measured. School context information was obtained from an objective audit. Logistic multilevel analysis was used. Results showed a high prevalence of GMC problems in Peruvian youth. Sex, age, geographical area of residence, biological maturation, nutritional status, stunting and physical fitness were important predictors of GMC problems. Moreover, there was an interaction between age, sex, and geographical area showing that girls, older subjects, and those from sea level regions were more likely to display GMC problems. The school context was less important in predicting GMC problems than the interplay between individual characteristics and the natural environment. The early identification as well as educational and pediatric care interventions are of utmost importance to reduce GMC problems among Peruvian children and adolescents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".