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Record W3087553983 · doi:10.2196/21286

Assessing the Effectiveness of Policies Relating to Breastfeeding Promotion, Protection, and Support in Southeast Asia: Protocol for a Mixed Methods Study

2020· article· en· W3087553983 on OpenAlexvenueno aff
Tuan T. Nguyen, Amy Weissman, Jennifer Cashin, Tran Thu Ha, Paul Zambrano, Roger Mathisen

Bibliographic record

VenueJMIR Research Protocols · 2020
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsBreastfeedingBreastfeeding promotionBusinessData collectionPromotion (chess)Environmental healthQualitative propertyLegislationEnforcementWorkforceMedicineEconomic growthNursingPolitical scienceEconomicsSociologyPediatrics

Abstract

fetched live from OpenAlex

BACKGROUND: Despite its well-known benefits, breastfeeding practices remain suboptimal worldwide, including in Southeast Asia. Many countries in the region have thus enacted policies, such as maternity protection and the World Health Assembly International Code of Marketing of Breast-milk Substitutes (the Code), that protect, promote, and support breastfeeding. Yet the impact of such national legislation on breastfeeding practices is not well understood. OBJECTIVE: This study aims to review the content, implementation, and potential impact of policies relating to maternity protection and the Code in Myanmar, the Philippines, Thailand, and Vietnam. METHODS: This mixed methods study includes a desk review, trend and secondary data analyses, and quantitative and qualitative data collection. Desk reviews will examine and compare the contents, implementation strategies, coverage, monitoring, and enforcement of national policies focusing on maternity protection and the Code in each country with global standards. Trend and secondary data analyses will examine the potential impact of these policies on relevant variables such as breast milk substitute (BMS) sales and women's workforce participation. Quantitative data collection and analysis will be conducted to examine relevant stakeholders' and beneficiaries' perceptions about these policies. In each country, we will conduct up to 24 in-depth interviews (IDI) with stakeholders at national and provincial levels and 12 employers or 12 health workers. Per country, we will survey approximately 930 women who are pregnant or have a child aged 0-11 months, of whom approximately 36 will be invited for an IDI; 12 partners of the interviewed mothers or fathers of children from 0-11 months will also be interviewed. RESULTS: This study, funded in June 2018, was approved by the Institutional Review Boards of the relevant organizations (FHI 360: April 16, 2019 and May 18, 2020; and Hanoi University of Public Health: December 6, 2019). The dates of data collection are as follows: Vietnam: November and December 2019, May and June 2020; the Philippines: projected August 2020; Myanmar and Thailand: pending based on permissions and funding. Results are expected to be published in January 2021. As of July 2020, we had enrolled 1150 participants. We will present a comparison of key contents of the policies across countries and against international standards and recommendations and a comparison of implementation strategies, coverage, monitoring, and enforcement across countries. We will also present findings from secondary data and trend data analyses to propose the potential impact of a new or amended policy. For the surveys with women, we will present associations between exposure to maternity protection or BMS promotion on infant and young child feeding practices and their determinants. Findings from IDIs will highlight relevant stakeholders' and beneficiaries' perceptions. CONCLUSIONS: This study will increase the understanding of the effectiveness of policy interventions to improve breastfeeding, which will be used to advocate for stronger policy adoption and enforcement in study countries and beyond. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/21286.

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 imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.621
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.367
GPT teacher head0.618
Teacher spread0.251 · 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 teacher head, not a consensus.

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

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

Citations19
Published2020
Admission routes1
Has abstractyes

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