Effect of Improving Housing Conditions on Early Childhood Health in Rural Sudan
Bibliographic record
Abstract
Improving housing sector in rural areas is important to improve health status of under-five children. Propensity score matching using nonparametric kernel estimates is used to examine the effect of improving rural structure of houses in rural Sudan and provide them with services like access to clean piped water, sanitation on improving under-five children health. The prevalence of diarrhoea and cough in rural Sudan are used as measures of health outcome and data from the Sudan Household Health Survey in 2010 is used. Our results show that providing houses with piped water can reduce prevalence of diarrhoea and cough by 22 and 24 percentage points, respectively. Gas cooking fuel reduces the prevalence rates by 26 and 29 percentage points, respectively. Construction materials of walls have strong impact on reducing the prevalence of both illnesses. We recommend that the quality of piped water should be observed and maintained in good standard to ensure that clean water is supplies to the household sector. Developing the housing sector in the rural has many advantages in improving early childhood health in Sudan and it should be one of the priorities of the government.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".