A systematic review of the benefits of breastfeeding against postpartum depression in low-middle-income countries
Bibliographic record
Abstract
BACKGROUND: The positive impact of breastfeeding against postpartum depression has been increasingly reported. However, no studies have systematically and critically examined current evidence on breastfeeding practices' influences on postpartum depression in LMICs. AIM: To review the influence of breastfeeding on postpartum depression in LMICs. METHODS: We searched original research in English published over the last ten years (2012 - 2022) within 8 databases: EBSCOhost, EMBASE, Pubmed, Sage Journals, Science Direct, APA PsycArticles, Taylor & Francis, Google Scholar, and citation tracking. The risk of bias assessment used The Newcastle Ottawa Scale and The Modified Jadad Scale. We followed the PRISMA statement after the protocol had been registered on the PROSPERO. The review included 21 of 11015 articles. RESULTS: Of 21 articles, 16 examined breastfeeding practices, 2 each investigated breastfeeding self-efficacy and breastfeeding education, and 1 each assessed breastfeeding attitude and breastfeeding support. 3 randomized control trials and 5 cohorts revealed that breastfeeding decreased the EPDS scores. However, 4 cross-sectional studies indicated that breastfeeding is nonsignificantly associated with postpartum depression. CONCLUSION: This review indicated that breastfeeding may alleviate or prevent postpartum depression. Our findings indicated that integrating breastfeeding-related programs and policies into postpartum depression prevention may benefit public health. REGISTRATION: PROSPERO (CRD42022315143).
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".