Early Childhood Education to Promote Health Equity: A Community Guide Systematic Review
Bibliographic record
Abstract
CONTEXT: A recent Community Guide systematic review found that early childhood education (ECE) programs improve educational, social, and health-related outcomes and advance health equity because many are designed to increase enrollment for high-risk children. This follow-up economic review examines how the economic benefits of center-based ECE programs compare with their costs. EVIDENCE ACQUISITION: Kay and Pennucci from the Washington State Institute for Public Policy, whose meta-analysis formed the basis of the Community Guide effectiveness review, conducted a benefit-cost analysis of ECE programs for low-income children in Washington State. We performed an electronic database search using both effectiveness and economic key words to identify additional cost-benefit studies published through May 2015. Kay and Pennucci also provided us with national-level benefit-cost estimates for state and district and federal Head Start programs. EVIDENCE SYNTHESIS: The median benefit-to-cost ratio from 11 estimates of earnings gains, the major benefit driver for 3 types of ECE programs (ie, state and district, federal Head Start, and model programs), was 3.39:1 (interquartile interval [IQI] = 2.48-4.39). The overall median benefit-to-cost ratio from 7 estimates of total benefits, based on all benefit components including earnings gains, was 4.19:1 (IQI = 2.62-8.60), indicating that for every dollar invested in the program, there was a return of $4.19 in total benefits. CONCLUSIONS: ECE programs promote both equity and economic efficiency. Evidence indicates there is positive social return on investment in ECE irrespective of the type of ECE program. The adoption of a societal perspective is crucial to understand all costs and benefits of ECE programs regardless of who pays for the costs or receives the benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.150 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".