A Decade of Breastfeeding Self-Efficacy Research: A Bibliometric Analysis using VOSviewer
Bibliographic record
Abstract
Introduction : A mother’s confidence in her ability to breasfeed, known as breastfeeding self efficacy (BSE) is a key predictor of exclusive breastfeeding duration. This study aimed to analyze the development of BSE research and provide insights for researchers and policymakers to improve future studies. Method : This bibliometric analysis was conducted using data from the Scopus database, one of the largest sources of peer reviewed literature. Articles were retrieved on August 7, 2024, using the keywords “Breastfeeding” AND “Self” AND “Efficacy.” A total of 1,006 documents were identified. After applying inclusion criteria (English language, article or review type, published between 2014 and 2023, open access, and final publication stage), 413 documents were selected for analysis. Data were analyzed using VOSviewer version 1.6.20 based on publication year, journal source, author, country of origin, and keywords. Results : Research on BSE has shown a consistent upward trend over the past decade, with the highest number of publications in 2023 (82 articles). The United States was the most productive country, followed by Brazil, Canada, the United Kingdom, China, and Indonesia. Most articles were published in journals focusing on lactation physiology, breastfeeding challenges, and evidence based interventions. The most cited studies emphasized the importance of partner support, coparenting, and breastfeeding safety during the COVID-19 pandemic. Keyword analysis revealed several underexplored areas, particularly the role of midwives, formula use, and BSE in mothers of preterm infants Conclusion : This study highlights the growing global attention to breastfeeding self-efficacy as a key factor in improving maternal and child health, and points to the need for further research on underexplored areas, including the role of midwives and the impact of alternative feeding practices, to support more effective interventions and policies.
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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 | 0.025 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.256 | 0.332 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".