A meta‐analytic review of the implementation characteristics in parenting interventions to promote early child development
Bibliographic record
Abstract
This review summarizes the implementation characteristics of parenting interventions to promote early child development (ECD) outcomes from birth to 3 years. We included 134 articles representing 123 parenting trials (PROSPERO record CRD42022285998). Studies were conducted across high-income (62%) and low-and-middle-income (38%) countries. The most frequently used interventions were Reach Up and Learn, Nurse Family Partnership, and Head Start. Half of the interventions were delivered as home visits. The other half used mixed settings and modalities (27%), clinic visits (12%), and community-based group sessions (11%). Due to the lack of data, we were only able to test the moderating role of a few implementation characteristics in intervention impacts on parenting and cognitive outcomes (by country income level) in the meta-analysis. None of the implementation characteristics moderated intervention impacts on cognitive or parenting outcomes in low- and middle-income or high-income countries. There is a significant need in the field of parenting interventions for ECD to consistently collect and report data on key implementation characteristics. These data are needed to advance our understanding of how parenting interventions are implemented and how implementation factors impact outcomes to help inform the scale-up of effective interventions to improve child development.
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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 | 0.022 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".