Digital Support Systems to Improve Child Health and Development in Peru: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Children living in low and middle-income countries (LMICs) are at greater risk for experiencing adversities that can undermine their health and early development. Recently launched digital early childhood development (ECD) programs attempt to support families with young children in their home environments using digital technologies. However, relatively little is known regarding the effectiveness of these new technologies. OBJECTIVE: The goal of this study is to rigorously assess the reach, effectiveness, and cost-effectiveness of a newly developed digital ECD platform called Afini. The Afini platform was designed to support parents of young children in low-resource settings to improve ECD and interact with caregivers through messenger services and a chatbot. METHODS: This is a 3-arm cluster randomized controlled trial. In total, 2471 caregivers and their 3- to 9-month-old children were enrolled in the study across 164 study clusters in the San Marcos, Cajabamba, and Cajamarca provinces of Peru. Clusters of participants were randomly assigned to 1 of 3 groups: a control group (72 community clusters and 980 caregiver-child dyads), a home visit intervention group (20 community clusters and 316 caregiver-child dyads), and an Afini intervention group (72 community clusters and 1175 caregiver-child dyads). Families in the control group receive no focused ECD intervention. The home visit group is receiving biweekly home visits by a trained field staff following the national ECD program (Programa Nacional Cuna Más) curriculum and training guidelines. Caregivers in the Afini group are receiving ECD activities and advice through the digital platform. The primary study outcome is children's overall development at the age of 2.5 years, using the internationally validated long form of the Global Scales for Early Development. Secondary outcomes include caregiver engagement; caregiver mental health; screen time; as well as caregiver reports of children's motor, cognitive, language, and socioemotional development measured through locally piloted and validated tools. RESULTS: Enrollment started in September 2021 and ended in March 2023. Endline assessments will take place between August 2023 and September 2024. CONCLUSIONS: This study is, to our knowledge, the first to rigorously assess the effectiveness and cost-effectiveness of digital ECD technologies in LMICs. Given the large number of children in LMICs currently receiving only limited external support, the evaluated platform has the potential to improve the short- and long-term well-being of millions of children and their parents globally. TRIAL REGISTRATION: ClinicalTrials.gov NCT05202106; https://clinicaltrials.gov/ct2/show/NCT05202106. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50371.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.030 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.090 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".