Maternal Responses to Anticipated Children's Negative Emotions and Social Adjustment in Early Childhood
Bibliographic record
Abstract
The goals of the present study were: 1) to describe and provide initial support for the validity of the Future Scenarios Questionnaire (FSQ), a new self-report questionnaire designed to measure parental responding to anticipated children’s negative emotions; and 2) to examine how maternal responses on the FSQ related to young children’s aggressive, asocial, and prosocial behaviors with peers. Further, this study examined whether the temperamental trait of negative affect moderated the relation between maternal responses on the FSQ and children’s social adjustment outcomes. Participants were 92 mothers of preschool-age children (43 boys and 49 girls; M age 61.5 months). Mothers provided ratings on the FSQ and child temperament ratings on the Child Behavior Questionnaire (CBQ; Rothbart, Ahadi, & Hershey, 1994). They also completed a range of measures which were included to assess the construct validity of the FSQ. These included measures of attachment representations, maternal mind-mindedness, perceived control, and alexithymia. Sixty-nine teachers provided ratings on the Child Behavior Scale (CBS; Ladd & Profilet, 1996) for children’s aggressive, asocial, and prosocial behaviors in the peer context. Factor analysis of the FSQ revealed two subscales: Encourage Emotion Expression (EEE) and Discourage Emotion Expression (DEE). Patterns of correlations among these subscales and the additional mother measures suggested that the FSQ demonstrates some construct validity. Further, the results of the moderation analyses showed that maternal responding on the FSQ interacts with negative affect in the prediction of child behaviors, however not in the hypothesized ways. In particular, encouraging emotion expression significantly predicted more asocial behavior and less prosocial behavior (approached significance), but only for children rated high in negative affect. Similarly, discouraging emotion expression significantly predicted less aggressive behavior only for high negative affect children. None of these relations was significant for children rated low in negative affect. The theoretical and practical implications of these findings are discussed in terms of the importance of considering child temperament in emotion socialization processes.
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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 | 0.000 | 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".