Micro-Economic Analysis of the Drivers of Under-Five Mortality in Kano Metropolis, Nigeria
Bibliographic record
Abstract
Nigeria is among the major countries contributing a significant quarter to death of children under the age of five in the world. This study was designed to analyze the drivers of child mortality in Kano Metropolis, Nigeria. Survey data was used, sourced via a structured questionnaire. Simple percentage and Negative Binomial Poisson Regression Model were used in the analysis of the data. It was found that education level of the household head, years of marriage experience, income level of the household, location, and vaccine are the significant drivers of child mortality in the study area. The results further revealed that, education level, years of marriage experience and location negatively influence under-five child mortality, while income of the household head and vaccine influence the under-five mortality of the household positively. The study recommends that, government should subsidize medical services and made it affordable to all individuals in the State, and that both government, NGOs and health institutions should embark on public enlightenment to educate the public on the importance of vaccines, natal care, and nutrition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".