Attachment and internalizing behavior in early childhood: A meta-analysis.
Bibliographic record
Abstract
Empirical research supporting the contention that insecure attachment is related to internalizing behaviors has been inconsistent. Across 60 studies including 5,236 families, we found a significant, small to medium effect size linking insecure attachment and internalizing behavior (observed d = .37, 95% CI [0.27, 0.46]; adjusted d = .19, 95% CI [0.09, 0.29]). Several moderator variables were associated with differences in effect size, including concurrent externalizing behavior, gender, how the disorganized category was treated, observation versus questionnaire measures of internalizing behavior, age of attachment assessment, time elapsed between attachment and internalizing measure, and year of publication. The association between avoidant attachment and internalizing behavior was also significant and small to moderate (d = .29, 95% CI [0.12, 0.45]). The effect sizes comparing resistant to secure attachment and resistant to avoidant attachment were not significant. In 20 studies with 2,679 families, we found a small effect size linking disorganized attachment and internalizing behavior (observed d = .20, 95% CI [0.09, 0.31]); however, the effect size was not significant when adjusted for probable publication bias (d = .12, 95% CI [-0.02, 0.23]). The existing literature supports the general notion that insecure attachment relationships in early life, particularly avoidant attachment, are associated with subsequent internalizing behaviors, although effect sizes are not strong.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".