Human Development Index and Under-five Mortality in the Middle East and North African Countries
Bibliographic record
Abstract
Background: This study aimed to examine the impact of the human development index (HDI) and other key macroeconomic variables on under-five mortality rates in the select Middle East and North African (MENA) countries from 2003 to 2019. Methods: The study used a panel data method to examine the impact of macroeconomic variables, such as HDI, gross national income per capita (GNI), urbanization rate, government health expenditure as a percentage of gross domestic product (GDP), and income distribution inequality index (Gini) on under-five mortality rates in the select MENA countries. Results: The HDI, GNI, urbanization rate, and government health expenditure share to GDP, have decreasing effects on the under-five mortality rate, while inequality in income distribution worsens health status and increases the under-five mortality rate. Conclusions: By strengthening the HDI and increasing economic growth, employment rate, and per capita income, people in the community will have access to health services, thereby reducing under-five mortality.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".