The RADAR coverage tool: developing a toolkit for rigorous household surveys for reproductive, maternal, newborn, and child health & nutrition indicators
Bibliographic record
Abstract
Population-based intervention coverage data are used to inform the design of projects, programs, and policies and to evaluate their impact. In low- and middle-income countries (LMICs), household surveys are the primary source of coverage data. Many coverage surveys are implemented by organizations with limited experience or resources in population-based data collection. We developed a streamlined survey and set of supporting materials to facilitate rigorous survey design and implementation. The RADAR coverage survey tool aimed to 1) rigorously measure priority reproductive, maternal, newborn, child health & nutrition coverage indicators, and allow for equity and gender analyses; 2) use standard, valid questions, to the extent possible; 3) be as light as possible; 4) be flexible to address users' needs; and 5) be compatible with the Lives Saved Tool for analysis of program impact. Early interactions with stakeholders also highlighted survey planning, implementation, and analysis as challenging areas. We therefore developed a suite of resources to support implementers in these areas. The toolkit was piloted by implementers in Tanzania and in Burkina Faso. Although the toolkit was successfully implemented in these settings and facilitated survey planning and implementation, we found that implementers must still have access to sufficient resources, time, and technical expertise in order to use the tool appropriately. This potentially limits the use of the tool to situations where high-quality surveys or evaluations have been prioritized and adequately resourced.
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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 | 0.058 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".