“The calm in the storm”: A scoping review of hospital-based peer support breastfeeding interventions
Bibliographic record
Abstract
International health bodies have called for nations to invest more in the promotion and support of breastfeeding. Peer models of breastfeeding support have shown to be an effective modality for a diversity of populations. A synthesis of in-person, peer breastfeeding support interventions in-hospital has not been done. This scoping review aimed to describe the nature and outcomes of hospital-based breastfeeding peer support reported in academic publications. The process included identifying research questions and relevant studies, selecting studies for analysis, charting data, and collating and summarizing results. We identified 24 articles for analysis, with descriptions of 12 peer breastfeeding interventions from the US and the UK. Through a qualitative content analysis of the studies, six categories emerged related to the interventions: intervention goals, theory, components, role of peers, program development and sustainability and evaluation. Most interventions were designed and implemented with a top-down approach, and utilized psychological theories of peer support. Findings from the quantitative and qualitative evaluations indicated the interventions demonstrated the capability to increase initiation, duration and exclusivity of breastfeeding. Positive psychosocial benefits were reported by mothers and positive health impacts for infants were detected. Hospital-based peer model is a promising practice that merits further implementation and study, in particular for families facing barriers to breastfeeding.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".