Estimation of the Family and Community Unobserved Heterogeneity Effects on the Risk of Under-Five Mortality in Nigeria using Frailty Model
Bibliographic record
Abstract
The Under-five mortality (U5M) rate is an important determinant of societal and national advancement- a key\n\nmarker of wellbeing, value, and access. In spite of efforts to identify the predictors of U5M to reduce its high level in\n\nNigeria, the problem remains a major cause for concern. This study estimated the potential role of\n\nunmeasured/unobserved factors at both family and community levels, using shared frailty models on the 2013 Nigeria\n\nDemographic and Health Survey (2013 NDHS) data. The Log-rank test was used to identify variables associated with\n\nU5M- hazard ratio estimates with P<0.05 were considered as statistically significant. Our findings suggested region,\n\nmarital status, place of residence and place of delivery were significant determinants of U5M in both frailty models. We\n\nalso found evidence of frailty effect on the risk, particularly at the community level- heterogeneity due to\n\nunmeasured/unobserved factors, which are generally ignored when we assess the risk using only observed variables.\n\nWe, therefore, suggest that to achieve the sustainable development goals relating to child health in Nigeria, more\n\nsignificant efforts should be directed at identifying more determinants, such as to reduce the influence of unobserved\n\nfactors and facilitate an extension of interventions to these factors.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".