Thinking, Feeling, and Moving in Kindergarten Children: How Motor Competence Shapes Executive Function Skills and Emotion Comprehension in Girls
Bibliographic record
Abstract
Background/Objectives: Increased screen time partially replaces social interaction, physical activity, and outdoor play in kindergarten children, leading to a risk of decreased cognitive, emotional, and motor skills. Children with high motor skills are more likely to have access to challenging joint activities that promote their cognitive and emotional development. This study examines the moderating role of motor competence in the relationship between executive function skills and emotion comprehension. Methods: A sample of 220 kindergarten children (101 girls, 119 boys) completed the NEPSY-II subtests and the ‘Dimensional Change Card Sort’ tool for executive function skills assessment, the Movement Assessment Battery for Children—Second Edition (MABC-2) for motor competence, and the Test of Emotion Comprehension (TEC) for emotion comprehension. Executive function skills and motor competence were assessed when children were in their penultimate year of kindergarten (children were aged on average 5 years 10 months), and emotion comprehension was assessed one year later, when children were in their final year of kindergarten. When children were in their penultimate year of kindergarten, caregivers also reported on children’s passive and active screen time, maternal education, and family income, which were used as control variables. Results: For girls, motor competence moderated the relationship between cognitive flexibility and later emotion comprehension. High motor competence amplified this relationship (B = 0.171; SE = 0.066; 95% CI [0.041, 0.302]; p = 0.011). For boys, there were no significant moderation effects. Conclusions: High motor competence can improve emotion comprehension in kindergarten girls. Emotional development may benefit from effective shared motor interventions for children.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".