Becoming Baby Friendly: A Complex Adaptive Systems Toolbox for Scaling up Breastfeeding Programs Globally
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,035 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,011 | 0,012 |
| Science ouverte | 0,003 | 0,013 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».