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Record W2934370901 · doi:10.1186/s12889-019-6584-9

Testing means to scale early childhood development interventions in rural Kenya: the Msingi Bora cluster randomized controlled trial study design and protocol

2019· article· en· W2934370901 on OpenAlexafffund
Jill Luoto, Italo López García, Frances E. Aboud, Lia C. H. Fernald, Daisy R. Singla

Bibliographic record

VenueBMC Public Health · 2019
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsSinai Health SystemUniversity of TorontoMcGill University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentUniversity of TorontoMedical Psychiatry Alliance
KeywordsBiostatisticsMedicineRandomized controlled trialProtocol (science)Cluster (spacecraft)Psychological interventionCluster randomised controlled trialPublic healthEpidemiologyEnvironmental healthResearch designEarly childhoodAlternative medicineNursingSurgeryPathologyDevelopmental psychologyStatistics

Abstract

fetched live from OpenAlex

BACKGROUND: Forty-three percent of children under five in low and middle-income countries (LMICs) experience compromised cognitive and psychosocial development. Early childhood development (ECD) interventions that promote parent-child psychosocial stimulation and nutrition activities can help remediate early disadvantages in child development and health outcomes, but are difficult to scale. Key questions are: 1) how to maximize the reach and cost-effectiveness of ECD interventions; 2) what pathways connect interventions to parental behavioral changes and child outcomes; and 3) how to sustain impacts long-term. METHODS: Msingi Bora ("good foundation" in Swahili) is a multi-arm cluster randomized controlled trial across 60 villages and 1200 households in rural Western Kenya that tests different, potentially cost-effective and scalable models to deliver an ECD intervention in biweekly sessions lasting 7 months. The curriculum integrates child psychosocial stimulation with hygiene and nutrition education. The multi-arm study will test the cost-effectiveness of two models of delivery: a group-based model versus a mixed model combining group sessions with personalized home visits. Households in a third study arm will serve as a control group. Each arm will have 20 villages and 400 households with a child aged 6-24 months at baseline. Primary outcomes are child cognitive and socioemotional development and home stimulation practices. In a 2 × 2 design among the 40 treatment villages, we will also test the role of including fathers in the intervention. We will estimate intention-to-treat and local average treatment effects, and examine mediating pathways using Mediation Analysis. One treatment arm will receive quarterly booster visits for 6 months following the end of the sessions. A follow-up survey 2 years after the end of the main intervention period will examine sustainability of outcomes and any spillover impacts onto younger siblings. Study protocols have been approved by the Maseno Ethics Review Committee (MUERC) in Kenya (00539/18) and by RAND's institutional review board. This study is funded by the National Institute for Child Health and Human Development (R01HD090045). DISCUSSION: Results can provide policymakers with rigorous evidence of how best to design ECD interventions in low-resource rural settings. TRIAL REGISTRATION: Clinical Trial NCT03548558 registered June 7, 2018 at clinicaltrials.gov; AEA-RCT registry AEARCTR-0002913.

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)0.040
metaresearch head score (Gemma)0.031
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 score0.040
Threshold uncertainty score0.214

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.031
Meta-epidemiology (narrow)0.0060.003
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0020.003
Science and technology studies0.0030.004
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0360.005

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 head0.057
GPT teacher head0.346
Teacher spread0.290 · 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

Citations13
Published2019
Admission routes2
Has abstractyes

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