Maternal and child health performance of a national community health workers’ program using large administrative databases: a quantitative case study of Afghanistan
Bibliographic record
Abstract
# Background Community health workers (CHW) are an established workforce in many low- and middle-income countries (LMICs). Some countries with national CHW programs are Brazil, India, Nepal, Ethiopia, Pakistan, Bangladesh, Iran, and Afghanistan. These large-scale CHW programs are often evaluated using data from individual CHWs rather than the program. There is a dearth of quantitative research on national CHW programs using health-related administrative datasets. The purpose of this paper is to describe geographical distribution of CHWs, the volume of their activities, and the relationship of their activities with recorded maternal and neonatal deaths in rural Afghanistan between 2009 and 2012. # Methods This paper is a quantitative analysis of national CHWs program using a large administrative database from the Afghan Ministry of Public Health linked to population census data from Afghanistan. # Results We found that CHWs and the aggregate volume of their activities have increased between 2009 and 2012 in rural Afghanistan. CHWs are not equitably distributed by population size in the 34 provinces of the country. Recorded maternal and neonatal deaths have shown an increase from 2009 to 2011 and a decrease from 2011 to 2012. # Conclusion Large administrative datasets are important data sources for research with a potential to offer valuable lessons for policymakers and health managers. Despite methodological and quality challenges, this study can be used as a baseline for future replications, a point of comparison for future research on national CHW programs for other countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".