Becoming Baby Friendly: A Complex Adaptive Systems Toolbox for Scaling up Breastfeeding Programs Globally
Bibliographic record
Abstract
Metrics exist to assist committed countries with measuring their existing environments for scaling up breastfeeding programs. Yet, no evidence‐based toolboxes exist to help countries to both assess as well as guide the development of national breastfeeding programs and their scaling‐up. The Becoming Breastfeeding Friendly (BBF)Toolbox provides an evidence‐based index (BBFI) as well as case studies designed to guide the development and tracking of large scale well‐coordinated multi‐sector national breastfeeding promotion programs that can be uniformly translated for use in low, middle, and high income countries worldwide Grounded in the evidence‐based Breastfeeding Gear Model (BFGM) complex adaptive systems framework, the BBFI was developed between August 2015 and January 2016 by Yale University researchers in collaboration with a 13‐member Technical Advisory Committee (TAG) comprised of academic (Bangladesh, Brazil, Canada, Ghana, Mexico, UK, USA), international agencies (WHO, UNICEF, PAHO), philanthropic organizations (Bill and Melinda Gates Foundation, Alive & Thrive), and policy experts in breastfeeding as well as members with expertise in metric development relevant to scaling up of health and nutrition programs. First, a review of the academic and grey literature was conducted by the BBF steering committee to identify metrics to assess country‐level readiness to scale up health initiatives within the areas of infant and young child feeding, food and nutrition, and newborn survival. The steering committee met regularly to develop and reach agreement on the key benchmarks and definitions for the BBFI, which were then proposed to the TAG. TAG members participated in the progressive assessment and revision of the BBFI following a Delphi consensus methodology. As part of this process, the TAG was convened for a three day highly intensive participatory meeting to discuss and reach initial consensus on the definitions and benchmarks for the metric. Following this meeting, TAG members continued to provide their expertise with the refinement of the BBFI, including weighting the benchmarks to help develop the BBF scoring algorithm. The resulting BBFI consists of eight gears that correspond to the BFGM: Advocacy (4 benchmarks); Political Will (3 benchmarks); Legislation & Policy (10 benchmarks); Funding & Resources (4 benchmarks); Training & Program Delivery (17 benchmarks); Promotion (3 benchmarks); Research & Evaluation (10 benchmarks); and Coordination, Goals, & Monitoring (3 benchmarks). Each gear contains gear‐related themes and associated benchmarks. A global BBFI total score as well as sub‐scores for each of the eight gears can be calculated from the benchmarks. The country environment as BBF is ranked as weak, moderate, strong and outstanding according global BBFI result. To aid countries in how to use their baseline enabling environment assessment to advocate for policy and program changes, the toolbox includes eighty‐seven case studies illustrating data‐driven decision making along with their references. The BBF toolbox is currently in the final stages of validation in Mexico and Ghana. Findings thus far indicate that BBF has a strong potential to influence effective scaling up of breastfeeding protection, promotion and support worldwide. Support or Funding Information Funded by the Family Larsson‐Rosenquist Foundation.
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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 | 0.035 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".