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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".