Enablers of vitamin A coverage among children under five years of age from multi-country analyses of global demographic and health surveys in selected LMIC and LIC countries in Africa and Asia: a random forest analysis
Bibliographic record
Abstract
Background: Vitamin A deficiency is a common form of micronutrient malnutrition. The estimated relative risks associated with vitamin A deficiency in children were 1.86 (95% CI 1.32–2.59) for measles mortality, 2.15 (95% CI 1.83–2.58) for diarrhoea mortality, 1.78 (95% CI 1.43–2.19) for malaria mortality, 1.13 (95% CI 1.01–1.32) for other infectious disease mortality. Vitamin A supplementation reduces night blindness, child morbidity and mortality.Methods: This paper tries to explore the socio-demographic causes of receipt of vitamin A in selected lower-middle-income and low income countries by analysing the data of the demographic and health surveys from 2012 and 2016 using PASW 18.0 software. Multivariate binary logistic regressions were conducted to explore the role of socio-demographic covariates in the receipt of vitamin A supplementation. In addition, random forest (RF) analyses were conducted using Python 3.6.Results: After adjusting for related socio-economic and demographic factors, mother’s work status and education and among mass media channels, exposure to television seems to play an important role in predicting receipt of vitamin A in the selected countries in Asia, while education of the mother was significantly associated with the receipt of vitamin A in the selected countries of Africa. In all the selected countries, the RF analyses revealed mother’s education followed by wealth index and mass media (TV), as the variable of most importance.Conclusions: It can be concluded that mother’s education and mass media seems to be working well in making the mothers aware about the vitamin A campaign, especially, the exposure to television. It also figures in the variable importance matrix in addition to wealth index.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".