Bibliographic record
Abstract
BACKGROUND: Malnutrition among children not only affects their health consequences but also does it burden their households’ finance especially in developing countries. This study evaluates the household risk of catastrophic health expenditure (CHE) due to malnutrition treatments among malnourished children in Nigeria, according to zones and wealth quintiles. We simulate the CHE risk among households with malnourished children who seek treatment. METHODS: The CHE risk due to malnutrition among treated was computed based on 1) the out-of-pocket (OOP) expenditure and indirect costs associated with malnutrition treatment, and 2) household consumption expenditures. I derived the CHE risk associated with malnutrition across zones and wealth quintiles in Nigeria, using secondary data sources for healthcare utilization, OOP expenditures, and consumption expenditures. RESULTS: There was a large variation of CHE risk according to zones and wealth quintiles. Among the poorest households, those in northeast and northwest would have the highest risk of CHE, up to 59 and 47%, while those in southwest would have the lowest risk of 14%. For all zones, as the wealth increases, the CHE risk would decrease. There would be zero or very little CHE risk among the richest households in any zones. INTERPRETATION: Nutrition interventions will help malnourished children improve their health status. However, we should also be wary about the financial consequences of the treatment that households should bear.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".