Temporal variation of chronic child malnutrition in the context of strengthening healthcare services in Burkina Faso: an Oaxaca-Blinder multivariate decomposition analysis
Bibliographic record
Abstract
Malnutrition seriously affects children's health, survival, and future productivity. According to the literature, increasing the supply of health services should help reduce the spread of malnutrition. This article analyses the sources of changes in the decline of chronic malnutrition during the 2000s, where there was an increase in the supply of health services in Burkina Faso. We used data from demographic and health surveys conducted in 2003 and 2010 in Burkina Faso. Malnutrition was defined according to the recommendations of the World Health Organization, while using standards of growth which are current and uniform for the two periods of study considered. We analyzed the source of temporal variation of chronic malnutrition through the Oaxaca-Blinder multivariate decomposition of the proportion of children suffering from chronic malnutrition. The analyses showed that the relative extent of chronic malnutrition in children decreased significantly, from 43.4% (CI 95%: 42.3-44.4) in 2003 to 34.7% (CI 95%: 33.6-35.9) in 2010. A quarter of this variation is due to a change in characteristics (composition effect), and the remaining 74.74% is due to a difference in coefficients (performance or behavior effect). Improved access to health services played a crucial role in reducing the scale of chronic malnutrition between 2003 and 2010. Other factors, such as educating mothers and urbanization, also contributed significantly. This study shows that improving access to health services is crucial for reducing chronic malnutrition. So, programs tackling child malnutrition must first and foremost ensure that children have access to health services.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".