Maternal depression and infant attachment security: A meta‐analysis
Bibliographic record
Abstract
The purpose of this study is to clarify the magnitude of the association between maternal depression and infant attachment nonsecurity, and to identify possible moderators of this relationship. An extensive literature search was conducted using multiple databases of both published and unpublished studies. A meta-analysis was conducted to determine the relationship between maternal depression and infant attachment security and to establish the effect size. The main findings from this meta-analysis, which included 42 studies, indicate that there is a small, yet significant, relationship between maternal depression and infant attachment nonsecurity. The rate of nonsecurity in infants of mothers with depression was approximately 20% higher than expected rates in a nonclinical population, and the association between depressive symptoms and nonsecurity was small, but significant. Infants of mothers with depression were nearly twice as likely to have a nonsecure attachment than were infants of healthy mothers. Depression measure and maternal sample source were identified as significant moderators of the odds ratio effect size. Results of this study demonstrate that there is a significant relationship between maternal depression and infant attachment nonsecurity, and suggest that interventions that focus on both maternal mental health and the attachment relationship are warranted.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".