Countries' experiences scaling up national breastfeeding, protection, promotion and support programmes: Comparative case studies analysis
Bibliographic record
Abstract
Scaling up effective interventions, policies and programmes can improve breastfeeding (BF) outcomes. Furthermore, considerable interest exists in learning from relatively recent successful efforts that can inform further scaling up, with appropriate adaptations, across countries. The purpose of this four-country case studies analysis was to examine why and how improvements in BF practices occurred across four contrasting countries; Burkina Faso, the Philippines, Mexico and the United States of America. Literature reviews and key informant interviews were conducted to document BF trends over time, in addition to why and how BF protection, promotion and support policies and programmes were implemented at a national level. A qualitative thematic analysis was conducted. The 'Breastfeeding Gear Model' and RE-AIM (Reach; Effectiveness; Adoption; Implementation; and Maintenance) frameworks were used to understand and map the factors facilitating or hindering the scale up of the national programmes and corresponding improvements in BF practices. Each of the studied countries had different processes and timing to implement and scale up programmes to promote, protect and support breastfeeding. However, in all four countries, evidence-based advocacy, multisectoral political will, financing, research and evaluation, and coordination were key to fostering an enabling environment for BF. Furthermore, in all countries, lack of adequate maternity protection and the aggressive marketing of the breast-milk substitutes industry remains a strong source of negative feedback loops that are undermining investments in BF programmes. Country-specific best practices included innovative legislative measures (Philippines), monitoring and evaluation systems (United States of America), engagement of civil society (Mexico) and behavior change communication BF promotion (Burkina Faso) initiatives. There is an urgent need to improve maternity protection and to strongly enforce the WHO Code of Marketing of Breast-Milk Substitutes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".