Interventions to Improve Breastfeeding Self-Efficacy and Resultant Breastfeeding Rates: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Maternal breastfeeding self-efficacy (BSE) is reflective of a mother's confidence in breastfeeding and is a modifiable factor that may improve breastfeeding rates. Breastfeeding self-efficacy theory purports that women with higher BSE will have better breastfeeding outcomes. Research aim: The aim of this systematic review was to explore the theoretical link between BSE and breastfeeding outcomes by investigating (a) if interventions to improve BSE were successful and (b) if improvements in BSE resulted in improved breastfeeding rates. METHODS: The authors performed a systematic search of 10 databases for studies that investigated the effect of interventions for mothers of full-term infants on BSE and breastfeeding rates. They used an inverse-variance, random-effects meta-analysis. RESULTS: Of 1,366 titles and abstracts identified, 58 full-text articles were screened and 11 met the study criteria. Compared with mothers in control groups, mothers in intervention groups had significantly higher BSE, scoring 4.86 points higher, 95% confidence interval [3.11, 6.61], at 2 months postpartum. Mothers in the intervention groups were 1.56 and 1.66 times more likely to be breastfeeding at 1 month and 2 months postpartum, respectively. Interventions that were implemented in the postpartum period, used combined delivery settings, or were informed by BSE theory had the greatest influence on breastfeeding outcomes. Meta-regression indicated that for each 1-point increase in the mean BSE score between the intervention and control groups, the odds of exclusive breastfeeding increased by 10% in the intervention group. CONCLUSION: Breastfeeding self-efficacy is a modifiable factor that practitioners can target to improve breastfeeding rates in mothers of full-term 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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.006 | 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".