Decline of exclusive breastfeeding: Practical advice and stronger policy compliance are needed in government health services in Lima, Peru
Bibliographic record
Abstract
In Peru, exclusive breastfeeding (EBF) in urban areas decreased from 64.5% in 2007 to 59.9% in 2010 despite a national infant feeding policy to protect, promote and support breastfeeding (BF). Health care providers (HCPs) play an essential role in influencing mothers’ feeding decisions. This study examined infant feeding advice provided by HCPs in government health services (N=3) in a peri‐urban area of Lima using a case‐study methodology. Semi‐structured interviews were conducted with 16 HCPs and 11 mothers of infants < 6 months of age. Seven mothers participated in two focus group discussions. The health service environment and educational activities were observed. Advice was provided via growth monitoring and medical visits, nutrition counseling sessions, home visits and talks. HCPs recommended EBF for 6 months but did not provide practical advice to address common problems. Barriers to providing adequate BF counseling by HCPs included heavy client load, inadequate in‐service training, poor counseling skills, and formula industry influence. Barriers to EBF among mothers included employment, perceived breast milk insufficiency, and infant formula promotion. Improved training of HCPs, stronger monitoring of compliance and implementation of national policy are needed in government health services to protect BF behaviors. Funding: CIHR GBH‐87063; McGill University
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".