Examining a Complex Model Linking Maternal Reflective Functioning, Maternal Meta-Emotion Philosophies, and Child Emotion Regulation
Bibliographic record
Abstract
Parental Reflective Functioning (PRF) refers to parents' ability to understand their children's behavior in light of underlying mental states such as thoughts, desires, and intentions. This study aimed to investigate whether maternal meta-emotion philosophies (i.e., emotion coaching, emotion dismissing) mediated the relation between maternal RF and child emotion regulation (ER). Additionally, children's genders and ages were examined as moderators of the associations between maternal RF and maternal meta-emotion philosophies. The sample comprises 667 Chinese mothers of children aged 4-6 years. Mothers completed questionnaires assessing their reflective functioning, emotion coaching and dismissing, and child emotion regulation. Results indicated both a direct link between maternal RF and child emotion regulation, as well as indirect pathways mediated by emotion coaching and dismissing. A child's gender and age also moderated the relations between maternal RF and meta-emotion philosophies. Specifically, the negative association between maternal pre-mentalizing modes and emotion coaching was stronger for mothers of girls than boys; whereas the negative association between maternal certainty of mental states and emotion dismissing, as well as the positive association between maternal interest and curiosity and emotion coaching were both stronger for mothers of younger children than older children. The findings suggest that emotion coaching and dismissing mediate the relation between maternal PRF and the emotion regulation of 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".