The Impact of Early Childhood Parenting Interventions on Child Learning: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Parenting is widely considered to be among the most important influences on early childhood (EC) development.But to what extent and under what circumstances can EC parenting programs improve child learning outcomes?While substantial progress has been made toward addressing these questions in recent years, there have been few attempts to systematically synthesize the evidence thus far with a view toward scaling and policy implications.This paper works toward filling this gap through a systematic review including both a quantitative meta-analysis and a detailed narrative analysis of randomized evaluations that test the impacts of EC parenting programs on learning outcomes.We find that these programs generate substantial effects across a wide range of contexts, and that the largest impacts are associated with programs that are conducted in low-or middle-income countries and that use curricula focusing on cognitive stimulation.Group parenting programs tend to yield effect sizes that are, on average, comparable to home visiting programs, typically at substantially lower costs.Qualitative analysis of evaluations of scaled interventions reveals that administrative implementation barriers rather than program ineffectiveness likely represent the primary impediment to stronger impact.We conclude by reflecting on implications for theory, policy, and priorities for future research.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.020 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".