Maternal Emotional Styles and Child Social Adjustment: Assessment, Correlates, Outcomes and Goodness of Fit in Early Childhood
Bibliographic record
Abstract
Abstract The goals of the present research were to develop a modified version of an existing self‐assessment questionnaire designed to measure parents’ emotional style and to examine how the aspects of child regulation may moderate the relation between the emotional styles and social outcomes in childhood. Participants in Study 1 were 140 mothers and children (73 males, 67 females, M age =56.0 months). The mothers completed the Maternal Emotional Styles Questionnaire (MESQ) to assess maternal emotional styles, and the Child Behavior Vignettes to assess parental goals across two scenarios. Participants in Study 2 were 50 mothers who were interviewed regarding their emotional styles using the Meta‐emotion Interview, and who also completed the MESQ. In Study 3, 100 mothers and children (41 males, 59 females, M =58.0 months) participated. The mothers completed the MESQ and the Child Behavior Questionnaire to assess the children's emotion and behavior regulation. Teachers completed the Child Behavior Scale to measure the indices of preschool adjustment. Results from Studies 1 and 2 indicated a two‐factor scale for the MESQ, with good psychometric properties (including stability, convergent validity, and construct validity). Results from Study 3 indicated different patterns of associations between maternal emotional styles, and child adjustment for well‐regulated versus dysregulated 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".