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Record W3039810755 · doi:10.2196/18521

Evaluation of a Package of Behaviour Change Interventions (Baduta Program) to Improve Maternal and Child Nutrition in East Java, Indonesia: Protocol for an Impact Study

2020· article· en· W3039810755 on OpenAlexvenueno aff
Michael J. Dibley, Ashraful Alam, Umi Fahmida, Iwan Ariawan, Christiana Rialine Titaley, Min Kyaw Htet, Rita Damayanti, Mu Li, Aang Sutrisna, Elaine Ferguson

Bibliographic record

VenueJMIR Research Protocols · 2020
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
FundersUniversity of Sydney
KeywordsPsychological interventionMedicineBehavior change communicationProgram evaluationEnvironmental healthFidelityIntervention (counseling)Randomized controlled trialBehavior changeFamily medicinePopulationNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Over the past decade, the prevalence of stunting has been close to 37% in children aged <5 years in Indonesia. The Baduta program, a multicomponent package of interventions developed by the Global Alliance for Improved Nutrition, aims to improve maternal and infant nutrition in Indonesia. OBJECTIVE: This study aims to assess the impact of the Baduta program, a package of health system strengthening and behavior change interventions, compared with the standard village health services on maternal and child nutrition. METHODS: The impact evaluation uses a cluster randomized controlled trial design with 2 outcome assessments. The first uses cross-sectional surveys of mothers of children aged 0-23 months and pregnant women before and after the interventions. The second is a cohort study of pregnant women followed until their child is 18 months from a subset of clusters. We will also conduct a process evaluation guided by the program impact pathway to assess coverage, fidelity, and acceptance. The study will be conducted in the Malang and Sidoarjo districts of East Java, Indonesia. The unit of randomization is the subdistricts. As random allocation of interventions to only 6 subdistricts is feasible, we will use constrained randomization to ensure balance of baseline covariates. The first intervention will be health system strengthening, including the Baby-Friendly Hospital Initiative, and training on counseling for appropriate infant and young child feeding (IYCF). The second intervention will be nutrition behavior change that includes Emo-Demos; a national television (TV) advertising campaign; local screening TV spots; a free, text message service; and promotion of low-cost water filters and hygiene practices. The primary study outcome is child stunting (low length-for-age), and secondary outcomes include length-for-age Z scores, wasting (low weight-for-length), anemia, child morbidity, IYCF indicators, and maternal and child nutrient intakes. The sample size for each cross-sectional survey is 1400 mothers and their children aged <2 years and 200 pregnant women in each treatment group. The cohort evaluation requires a sample size of 340 mother-infant pairs in each treatment group. We will seek Gatekeeper consent and written informed consent from the participants. The intention-to-treat principle will guide our data analysis, and we will apply Consolidated Standards of Reporting Trials guidelines for clustered randomized trials in the analysis. RESULTS: In February 2015, we conducted a baseline cross-sectional survey on 2435 women with children aged <2 years and 409 pregnant women. In February 2017, we conducted an end-line survey on 2740 mothers with children aged <2 years and 642 pregnant women. The cohort evaluation began in February 2015, with 729 pregnant women, and was completed in December 2016. CONCLUSIONS: The results of the program evaluation will help guide policies to support effective packages of behavior change interventions to prevent child stunting in Indonesia. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/18521.

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)0.051
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.051
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.032
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0030.002
Science and technology studies0.0040.003
Scholarly communication0.0030.003
Open science0.0040.004
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0460.009

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 head0.540
GPT teacher head0.623
Teacher spread0.083 · 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 designNot applicable
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

Citations13
Published2020
Admission routes1
Has abstractyes

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