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Record W4386003856 · doi:10.1016/j.cdnut.2023.101988

“Stronger with Breastmilk Only” Initiative in 5 African Countries: Case Study on the Implementation Process and Contribution to the Enabling Environment for Breastfeeding

2023· article· en· W4386003856 on OpenAlexaff
Isabelle Michaud‐Létourneau, Marion Gayard, Jacqueline Wassef, Nathalie Likhite, Manisha Tharaney, Aïta Sarr Cissé, Anne-Sophie Le Dain, Arnaud Laillou, Maurice Zafimanjaka, Médiatrice Kiburente, Estelle Bambara, Sunny S. Kim, Purnima Menon

Bibliographic record

VenueCurrent Developments in Nutrition · 2023
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsUniversité de Montréal
FundersDaewoo Shipbuilding & Marine EngineeringUNICEFInternational Fine Particle Research InstituteBill and Melinda Gates Foundation
KeywordsBreastfeedingSierra leonePsychological interventionLegislationEconomic growthDemocracyPolitical scienceProcess (computing)MedicineBusinessNursingSocioeconomicsPediatricsSociologyPoliticsEconomicsComputer science

Abstract

fetched live from OpenAlex

Background: The practice of giving water before 6 mo of age is the biggest barrier to exclusive breastfeeding in West and Central Africa. To address this challenge, a regional initiative, "Stronger with Breastmilk Only" (SWBO), was rolled out at country level in several countries of the region. Objective: We examined the implementation process of the SWBO initiative and the contribution of its advocacy component to a more supportive environment for breastfeeding policies and programs. Methods: This study was based on 2 assessments at the national level carried out in 5 countries (Burkina Faso, Chad, Democratic Republic of the Congo, Senegal, and Sierra Leone) using qualitative methods. We combined 2 evaluative approaches (contribution analysis and outcome harvesting) and applied 2 theoretical lenses (Breastfeeding Gear Model and Consolidated Framework for Implementation Research) to examine the implementation process and the enabling environment for breastfeeding. Data sources included ∼300 documents related to the initiative and 43 key informant interviews collected between early 2021 and mid-2022. Results: First, we show how a broad initiative composed of a set of combined interventions targeting multiple levels of determinants of breastfeeding was set up and implemented. All countries went through a similar pattern of activities for the implementation process. Second, we illustrate that the initiative was able to foster an enabling environment for breastfeeding. Progress was achieved notably on legislation and policies, coordination, funding, training and program delivery, and research and evaluation. Third, through a detailed contribution story of the case of Burkina Faso, we illustrate more precisely how the initiative, specifically its advocacy component, contributed to this progress. Conclusion: This study shed light on how an initiative combining a set of interventions to address determinants of breastfeeding at multiple levels can be implemented regionally and contributes to fostering an enabling environment for breastfeeding at scale.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.003
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.050
GPT teacher head0.365
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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