The Influence of Breastfeeding Educational Interventions on Breast Engorgement and Exclusive Breastfeeding: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Breast engorgement and breast pain are the most common reasons for the early cessation of exclusive breastfeeding by mothers. Research Aims (1) To examine the influence of breastfeeding educational interventions on breast engorgement, breast pain, and exclusive breastfeeding; and (2) to identify effective components for implementing breastfeeding programs. Methods Randomized controlled trials of breastfeeding educational interventions were searched using five English and five Chinese databases. Eligible studies were independently evaluated for methodological quality, and data were extracted by two investigators. In total, 22 trials were identified, and 3,681 participants were included. A random-effects model was used to pool the results, and a subgroup analysis and meta-regression analysis were conducted. Results Breastfeeding education had a significant influence on reducing breast engorgement at postpartum 3 days (odds ratio [OR]: 0.27, 95% CI [0.15, 0.48] p < .001), 4 days (OR: 0.16, 95% CI [0.11, 0.22], p < .001), and 5–7 days (OR: 0.24, 95% CI [0.08, 0.74], p = .013) and breast pain (standardized mean difference: −1.33, 95% CI [−2.26, −0.40]) at postpartum 4–14 days. Participants who received interventions had higher odds of exclusive breastfeeding. Breastfeeding educational interventions provided through lecture combined with skills practical effectively reduced breast engorgement (OR: 0.21; 95% CI [0.15, 0.28]; p = .001) and improved exclusive breastfeeding at postpartum 1–6 weeks (OR: 2.16; 95% CI [1.65, 2.83]; p = .001). Conclusions Breastfeeding educational interventions have been effective in reducing breast engorgement, breast pain, and improved exclusive breastfeeding. A combination of knowledge and skill-based education has been beneficial for sustaining exclusive breastfeeding by mothers.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".