Research Trends on Self-Efficacy in Breastfeeding Mothers During 2000-2023: A Bibliometric Analysis
Bibliographic record
Abstract
Self-efficacy is important in supporting breastfeeding behaviors and enhancing breastfeeding practices. This study aimed to explore research trends, identify knowledge gaps, and provide a comprehensive overview of breastfeeding self-efficacy. Data were retrieved from the Scopus online database on March 20, 2024, encompassing articles published in English between 2000 and 2023. The search strategy focused on articles containing the keywords "self-efficacy," "breastfeeding," and "mother." VOSviewer version 1.6.19 was then used to map the data and visually identify research clusters. The analysis yielded a significant increase in breastfeeding self-efficacy research over the past 2 decades. The United States led in publication numbers (117 documents), while Canada dominated citations (2792 citations). Furthermore, the analysis identified 8 critical themes in 8 different clusters, encompassing various aspects of breastfeeding, including determinants, behaviors, interventions, and mental health considerations during lactation, and employed research methodologies. One crucial gap identified in this study pertains to low breastfeeding social support. This includes a lack of support from peer groups, social networks, and social media platforms. Addressing this gap can be a valuable reference point for developing future interventions to bolster breastfeeding self-efficacy. Bibliometric analysis contributes to exploring research trends, identifying knowledge gaps, and providing a comprehensive overview of breastfeeding self-efficacy.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.148 | 0.220 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".