Interventions to Improve Maternal-Infant Relationships in Mothers With Postpartum Mood Disorders
Bibliographic record
Abstract
INTRODUCTION: During the postpartum period, women may have changes in their mental health and experience postpartum mood disorders. Postpartum depression (PPD) is an especially prevalent postpartum mood disorder, affecting 10% to 15% of new mothers. Although PPD has detrimental effects on women's health, it can also affect maternal-infant attachment, bonding, and interaction, which influence the maternal-infant relationship and can lead to poor outcomes for infants later in life. The purpose of this review is to identify effective strategies for improving the maternal-infant relationship for mothers with postpartum mood disorders. METHODS: A literature search was conducted via three databases: PubMed, Cochrane Library, and Medline using key search terms. A total of 1,347 articles were scanned to determine their relevance; 19 articles were selected for review. Inclusion criteria included articles in English that focused in the postpartum period and measured outcomes related to the maternal-infant relationship. RESULTS: Infant massage appears to benefit the maternal-infant relationship, whereas psychotherapy and education had mixed results. Pharmacological interventions were not found to improve maternal-infant relationships. Family involvement was shown to improve infant attachment, but not the maternal-infant relationship. CLINICAL IMPLICATIONS: Nurses should be aware of the importance of including interventions targeted at improving the maternal-infant relationship for women with postpartum mood disorders, especially PPD. However, data are limited, thus more research is needed to develop evidence-based strategies that can be implemented to support women experiencing postpartum mood disorders and their infants.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".