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Record W4413862756 · doi:10.2196/75796

Social Transfers for Exclusive Breastfeeding in Brazil: Protocol for a Randomized Controlled Trial

2025· article· en· W4413862756 on OpenAlexvenueno aff
Stéphanie Khoury, Alexandra Brentani, Helena Brentani, Jarlei Fiamoncini, Rossana Pulcineli Vieira Francisco, Ana Carolina da Silva Onofre, Günther Fink, Jordyn T. Wallenborn

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsnot available
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsPreprintProtocol (science)BreastfeedingRandomized controlled trialMedicinePsychologyComputer scienceAlternative medicinePediatricsWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: According to the World Health Organization's infant and young child feeding guidelines, infants should be exclusively breastfed for the first 6 months of life. Despite public health campaigns to increase exclusive breastfeeding (EBF) rates, socioeconomic inequities persist among low-income breastfeeding mothers, especially in countries with large wealth and health gaps, such as Brazil. Social transfer programs are initiatives that provide financial support to individuals or households to improve their well-being and reduce financial burdens. These may be conditional, requiring recipients to meet specific criteria to receive the transfer, or unconditional, in which recipients receive the transfer without prerequisites. Evidence suggests that conditional and unconditional social transfers may help increase EBF rates while addressing the economic challenges breastfeeding mothers face. A randomized controlled trial (RCT) conducted in Vientiane, Lao People's Democratic Republic, found that a social transfer program significantly improved both EBF rates at 6 months and EBF duration. Building on this study, we aim to evaluate the impact of this intervention in a different socioeconomic and cultural context. OBJECTIVE: This protocol aims to implement an RCT to assess whether conditional and unconditional social transfers improve EBF rates at 6 months postpartum for mothers in low-income communities in São Paulo, Brazil. METHODS: A prospective RCT will be conducted among 400 mothers who gave birth in the last 72 hours and plan to exclusively breastfeed. Participants will be recruited in São Paulo at the University Hospital of São Paulo and Amparo Maternal. Participants will be randomly assigned to one of the following groups: (1) control group-no social transfer; (2) intervention group 1-an unconditional social transfer at 6 months postpartum; and (3) intervention group 2-a social transfer at 6 months postpartum, conditional upon mothers' EBF. All groups will receive educational materials supporting EBF. The study will have visits at birth, 1 month, 6 months, 1 year, and 2 years and will include a questionnaire and biological collections of breast milk samples, infant fecal samples, and blood samples (finger pricks) from both the mother and infant. The main study outcomes are the prevalence of EBF at 6 months and the duration of EBF across the 3 groups, where we hypothesize higher rates of EBF among mothers in the conditional group. RESULTS: Recruitment began on March 6, 2024. As of September 2025, we enrolled 204 participants. Our goal is to recruit 400 mother-infant dyads by October 2025, with study visits expected to be completed by October 2027. CONCLUSIONS: We hypothesize that the Social Transfers for Exclusive Breastfeeding in Brazil (STEBB) intervention will positively impact breastfeeding mothers in São Paulo. If successful, the program may inform national policy to enhance Brazil's existing social transfer program for new mothers. TRIAL REGISTRATION: ClinicalTrials.gov NCT06157697; https://clinicaltrials.gov/study/NCT06157697. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/75796.

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)0.033
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.085
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.035
Meta-epidemiology (narrow)0.0060.003
Meta-epidemiology (broad)0.0140.007
Bibliometrics0.0030.004
Science and technology studies0.0040.004
Scholarly communication0.0050.004
Open science0.0030.002
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0850.010

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 head0.182
GPT teacher head0.597
Teacher spread0.415 · 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 designRandomized trial
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

Citations0
Published2025
Admission routes1
Has abstractyes

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