Effective treatment for postpartum depression is not sufficient to improve the developing mother–child relationship
Bibliographic record
Abstract
Maternal depression is prevalent, and puts children at risk. Little evidence addresses whether treatment for maternal depression is sufficient to improve child outcomes. An experiment was conducted testing whether psychotherapeutic treatment for mothers, suffering from major depression in the postpartum period, would result in improved parenting and child outcomes. Participants included depressed women randomly assigned to interpersonal psychotherapy (n = 60) or to a waitlist (n = 60), and a nondepressed comparison group (n = 56). At 6 months, depressed mothers were less responsive to their infants, experienced more parenting stress, and viewed their infants more negatively than did nondepressed mothers. Treatment affected only parenting stress, which improved significantly but was still higher than that for nondepressed mothers. Eighteen months later, treated depressed mothers still rated their children lower in attachment security, higher in behavior problems, and more negative in temperament than nondepressed mothers. Initial response to treatment did not predict reduced risk for poor child outcomes. Early maternal negative perceptions of the child predicted negative temperament and behavior problems 18 months after treatment. Treatment for depression in the postpartum period should target the mother–infant relationship in addition to the mothers' depressive symptoms.This work was supported by National Institute of Mental Health Grant MH 50524 to the second author. Additional support was provided by NIMH Traineeship MH 15755, through the Institute of Child Development, University of Minnesota and by the Canada Research Chairs program. We gratefully acknowledge the assistance of Grazyna Kochanska, our numerous undergraduate coders, and our generous and dedicated participants.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".