Configurations of mother‐child and father‐child attachment as predictors of internalizing and externalizing behavioral problems: An individual participant data (IPD) meta‐analysis
Bibliographic record
Abstract
An unsettled question in attachment theory and research is the extent to which children's attachment patterns with mothers and fathers jointly predict developmental outcomes. In this study, we used individual participant data (IPD) meta-analysis to assess whether early attachment networks with mothers and fathers are associated with children's internalizing and externalizing behavioral problems. Following a pre-registered protocol, data from 9 studies and 1,097 children (mean age: 28.67 months) with attachment classifications to both mothers and fathers were included in analyses. We used a linear mixed effects analysis to assess differences in children's internalizing and externalizing behavioral problems as assessed via the average of both maternal and paternal reports based on whether children had two, one, or no insecure (or disorganized) attachments. Results indicated that children with an insecure attachment relationship with one or both parents were at higher risk for elevated internalizing behavioral problems compared with children who were securely attached to both parents. Children whose attachment relationships with both parents were classified as disorganized had more externalizing behavioral problems compared to children with either one or no disorganized attachment relationship with their parents. Across attachment classification networks and behavioral problems, findings suggest (a) an increased vulnerability to behavioral problems when children have insecure or disorganized attachment to both parents, and (b) that mother-child and father-child attachment relationships may not differ in the roles they play in children's development of internalizing and externalizing behavioral problems.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.033 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".