Developmental correlates and predictors of emotional availability in mother–child interaction: A longitudinal study from infancy to middle childhood
Bibliographic record
Abstract
In this investigation we examined the developmental correlates and predictors of maternal emotional availability in interactions with their 7-year-old children among a sample of families at psychosocial risk. We found developmental coherence in maternal interactive behavior, and in the relations between maternal emotional availability and children's functioning in middle childhood. Mothers and children were observed at home and in a laboratory playroom in infancy to assess maternal interactive behavior and child attachment security. When children were 7 years of age, dyads were observed in the lab; maternal emotional availability was coded using the Emotional Availability Scales, and children's disorganized and controlling attachment behavior was assessed. Classroom teachers reported on children's behavior problems; at age 8, children reported on their depressive symptoms. Results showed that aspects of maternal emotional availability (sensitivity, nonhostility, nonintrusiveness [passive/withdrawn behavior]) were associated with children's functioning in middle childhood: (a) controlling and disorganized attachment behavior, (b) behavior problems in school, and (c) self-reported depressive symptoms. Maternal emotional availability in childhood was predicted by early mother-infant relationship dysfunction (maternal hostility, disrupted communication, and infant attachment insecurity).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".