In-person versus remote (mHealth) delivery for a responsive parenting intervention in rural Kenya: a cluster randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: An estimated 43% of children under age 5 in low- and middle-income countries (LMICs) experience compromised development due to poverty, poor nutrition, and inadequate psychosocial stimulation. Numerous early childhood development (ECD) parenting interventions have been shown to be effective at improving ECD outcomes, at least in the short-term, but they are (a) still too expensive to implement at scale in low-resource and rural settings, and (b) their early impacts tend to fade over time. New approaches to deliver effective ECD parenting interventions that are low-cost, scalable, and sustainable are sorely needed. METHODS: Our study will experimentally test a traditional in-person group-based delivery model for an evidence-based ECD parenting intervention against a hybrid-delivery model that increasingly substitutes in-person meetings with remote (mHealth) delivery via smartphones, featuring audiovisual content and WhatsApp social interactions and learning. We will assess the relative effectiveness and cost of this hybrid-delivery model compared to in-person delivery and will extend the interventions over two years to increase their ability to sustain changes in parenting behaviors and ECD outcomes longer-term. Our evaluation design is a cluster Randomized Controlled Trial (cRCT) across 90 villages and approximately 1200 households. Midline and endline surveys collected 12 and 24 months after the start of the interventions, respectively, will examine short- and sustained two-year intention-to-treat impacts on primary outcomes. We will also examine the mediating pathways using mediation analysis. We hypothesize that a hybrid-delivery ECD intervention will be lower in cost, but remote interactions among participants may be an inferior substitute for in-person visits, leaving open the question of the most cost-effective program. DISCUSSION: Our goal is to determine the best model to maximize the intervention's reach and sustained impacts to improve child outcomes. By integrating delivery into the ongoing operations of local Community Health Promoters (CHPs) within Kenya's rural health care system, and utilizing new low-cost technology, our project has the potential to make important contributions towards discovering potentially scalable, sustainable solutions for resource-limited settings. TRIAL REGISTRATION: NCT06140017 (02/08/2024) AEARCTR0012704.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".