Maternal Emotion Socialization on Children's Social Competence
Bibliographic record
Abstract
Maternal emotion socialization plays a crucial role in children's social and emotional development (Chan et al. 2024). Previous studies have consistently shown that maternal emotion coaching is positively associated with children's social competence (Chan et al., 2024; Perkins et al., 2022; Rogers et al., 2016). However, less is known about how maternal emotional characteristics influence their emotion coaching and dismissing practices. The current study investigates whether maternal emotional clarity is associated with children's social competence through its relationship with maternal emotion coaching and dismissing behaviors. Mothers (N = 163) completed the Toronto Alexithymia Scale, Maternal Emotional Style Questionnaire, and the Early School Behavior Scale to report maternal emotional clarity, maternal emotion coaching and emotion dismissing practices, and child social competence, respectively. Structural equation modeling tested the hypothesized mediational relationships among these variables. Results indicated that mothers who had greater difficulty describing their own emotions were more likely to engage in emotion dismissing behaviors. Additionally, maternal emotion coaching was positively related to children's social competence. Interestingly, maternal emotion coaching and emotion dismissing behaviors were positively associated, suggesting that mothers who frequently use emotion coaching might simultaneously exhibit higher levels of dismissing behaviors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".