Configurations of mother–child and father–child attachment relationships as predictors of child language competence: An individual participant data meta-analysis
Bibliographic record
Abstract
Abstract An individual participant data meta-analysis was conducted to test pre-registered hypotheses about how the configuration of attachment relationships to mothers and fathers predicts children's language competence. Data from seven studies (published between 1985 and 2014) including 719 children (M age: 19.84 months; 51% female; 87% White) were included in the linear mixed effects analyses. Mean language competence scores exceeded the population average across children with different attachment configurations. Children with two secure attachment relationships had higher language competence scores compared to those with one or no secure attachment relationships (d = .26). Children with two organized attachment relationships had higher language competence scores compared to those with one organized attachment relationship (d = .23), and this difference was observed in older versus younger children in exploratory analyses. Mother–child and father–child attachment quality did not differentially predict language competence, supporting the comparable importance of attachment to both parents in predicting developmental outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.043 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".