A Community-based Responsive Caregiving Program Improves Neurodevelopment in Two-year Old Children in a Middle-Income Country, Grenada, West Indies
Bibliographic record
Abstract
Many young children in low- and middle-income countries (LMICs) are at risk of developmental delays. Early child development (ECD) interventions have been shown to improve outcomes, but few interventions have targeted culturally normative violence such as corporal punishment (CP). We partnered with an existing community-based ECD organization in the LMIC of Grenada to implement a parallel controlled-trial single-blind responsive caregiving intervention that educates parents about the developing brain and teaches alternatives to corporal punishment while building parental self-regulation skills and strengthening social-emotional connections between parent and child. Parents and primary caregivers with children under age two were eligible. Allocation to the intervention and waitlist control arms was unblinded and determined by recruitment into the program. Neurodevelopment was assessed by blinded testers when each child turned age two. Primary comparison consisted of neurodevelopmental scores between the intervention and waitlist control groups (Clinicaltrials.gov registration xxx NCT04697134). Secondary comparison consisted of changes in maternal mental health, home environment, and attitudes towards CP. Children in the intervention group (n = 153) had significantly higher scores than children in the control group (n = 151) on measures of cognition (p = .022), fine motor (p .0001), gross motor (p = .015), and language development (p = .013). No difference in secondary outcomes, including CP, was detected.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".