Strengthening effectiveness evaluations to improve programs for women, children and adolescents
Bibliographic record
Abstract
A full understanding of the pathways from efficacious interventions to population impact requires rigorous effectiveness evaluations conducted under realistic scale-up conditions at country level. In this paper, we introduce a deductive framework that underpins effectiveness evaluations. This framework forms the theoretical and conceptual basis for the 'Real Accountability: Data Analysis for Results' (RADAR) project, intended to address gaps in guidance and tools for the evaluation of projects being implemented at scale to reduce mortality among women and children. These gaps include needs for a framework to guide decisions about evaluations and practical measurement tools, as well as increased capacity in evaluation practice among donors and program planners at global, national and project levels. RADAR aimed to improve the evidence base for program and policy decisions in reproductive, maternal, newborn and child health and nutrition (RMNCH&N). We focus on five linked methodological steps - presented as core evaluation questions - for designing and implementing effectiveness evaluation of large-scale programs that support both the needs of program managers to improve their programs and the needs of donors to meet their accountability responsibilities. RADAR has operationalized each step with a tool to facilitate its application. We also describe cross-cutting methodological issues and broader contextual factors that affect the planning and implementation of such evaluations. We conclude with proposals for how the global RMNCH&N community can support rigorous program evaluations and make better use of the resulting evidence.
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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 | 0.512 | 0.567 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".