Measuring the implementation of early childhood development programs
Bibliographic record
Abstract
In this paper we describe ways to measure variables of interest when evaluating the implementation of a program to improve early childhood development (ECD). The variables apply to programs delivered to parents in group sessions and home or clinic visits, as well as in early group care for children. Measurements for four categories of variables are included: training and assessment of delivery agents and supervisors; program features such as quality of delivery, reach, and dosage; recipients' acceptance and enactment; and stakeholders' engagement. Quantitative and qualitative methods are described, along with when measures might be taken throughout the processes of planning, preparing, and implementing. A few standard measures are available, along with others that researchers can select and modify according to their goals. Descriptions of measures include who might collect the information, from whom, and when, along with how information might be analyzed and findings used. By converging on a set of common methods to measure implementation variables, investigators can work toward improving programs, identifying gaps that impede the scalability and sustainability of programs, and, over time, ascertain program features that lead to successful outcomes.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".