Social Avoidance and Social Adjustment: The Moderating Role of Emotion Regulation and Emotion Lability/Negativity Among Chinese Preschool Children
Bibliographic record
Abstract
The present study explored the role of emotion regulation and emotion lability/negativity as a moderator in the relation between child social avoidance and social adjustment (i.e., interpersonal skills, asocial behavior, peer exclusion) in Chinese culture. Participants were N = 194 children (102 boys, 92 girls, Mage = 70.82 months, SD = 5.40) recruited from nine classrooms in two public kindergartens in Shanghai, People’s Republic of China. Multi-source assessments were employed with mothers rating children’s social avoidance and teachers rating children’s emotion regulation, emotion lability/negativity and social adjustment outcomes. The results indicated that the relations between social avoidance and social adjustment difficulties were more negative among children lower in emotion regulation, but not significant for children with higher emotion regulation. In contrast, the relations between social avoidance and social adjustment difficulties were more positive among children higher in emotion lability/negativity, but not significant for children with lower emotion lability/negativity. This study informs us about how emotion regulation and emotion lability/negativity are jointly associated with socially avoidant children’s development. As well, the findings highlight the importance of considering the meaning and implication of social avoidance in Chinese culture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".