Building the capacity of health professionals in developing countries through the use of public domain software to analyze Demographic and Health Survey data
Bibliographic record
Abstract
Nationally representative data sets, such as the Demographic and Health Surveys (DHS), are valuable resources for evaluating country trends within the field of nutrition. Teaching health professionals to analyze DHS data using public domain software enables them to build capacity for: evaluating national and local trends of key indicators, and understanding risk factors for key health and nutrition outcomes. We piloted the efficacy of teaching Ghanaian health professionals how to analyze DHS data, including children's anthropometry, using Epi Info for Windows through an intensive 4‐day workshop at the University of Ghana, Legon. We also developed a 200+ page manual that was provided to all participants. Twenty‐two participants traveled from several regions in Ghana to attend the workshop. Over half of participants represented the Ghanaian Health Services, while the rest were from research organizations, NGOs, as well as educational and health institutions. Evaluations indicated the workshop was successful at meeting its objectives. Replication of this workshop worldwide has important implications for nutrition programming and research in developing countries. Funded by a USDA predoctoral fellowship, The UConn College of Agriculture and Natural Resources, and the US National Institutes of Health grant # HD43620 .
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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 | 0.104 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 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".