Shyness and Socio-emotional Adjustment Difficulties in Urban Chinese Kindergartners: The Moderating Role of Child Effortful Control
Bibliographic record
Abstract
Research Findings: The goal of the present study was to examine the moderating role of child effortful control (EC) in the relation between shyness and social-emotional adjustment difficulties among young Chinese children. Participants included 195 children (117 boys, 78 girls, Mage = 4.28 years, SD = 0.31) enrolled in 6 classes attending kindergartens, Shanghai, People’s Republic of China. Mothers provided ratings of their children’s temperament (shyness, EC) at Time 1, teachers and mothers rated children’s social-emotional adjustment at Time 2 (one and a half years later). Results from SPSS PROCESS MACRO revealed several significant interaction effects between shyness and EC in the prediction of outcome variables. Follow-up simple slope analyses indicated that among children with higher levels of EC, shyness was negatively related to mother-reported social-emotional adjustment difficulties, while among children with lower levels of EC, shyness was not associated with mother-reported social-emotional adjustment difficulties. However, among children with lower levels of EC, shyness was positively associated with teacher reported social-emotional adjustment difficulties. In contrast, among children with higher levels of EC, shyness was not associated with teacher-reported social-emotional adjustment difficulties. Practice or Policy: The findings provide evidence to suggest that the combination of shyness and EC may contribute to children’s social adjustment, which in turn may promote or attenuate socio-emotional adjustment difficulties.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".