Child mortality and poverty in three world regions (the West, Asia and Sub-Saharan Africa) 1988–2010: Evidence of relative intra-regional neglect?
Bibliographic record
Abstract
AIMS: Poverty kills children. This study assesses the relationship between poverty and child mortality rates (CMRs) in 71 societies from three world regions to determine whether some countries, relative to their region, neglect their children. METHODS: Spearman rank order correlations were calculated to determine any association between the CMR and poverty data, including income inequality and gross national income. A current CMR one standard deviation (SD) above or below the regional average and a percentage change between 1988 and 2010 were used as the measures to assess the progress of nations. RESULTS: There were positive significant correlations between higher CMRs and relative poverty measures in all three regions. In Western countries, the current CMRs in the USA, New Zealand and Canada were 1 SD below the Western mean. The narrowest income inequalities, apart from Japan, were seen in the Scandinavian nations alongside low CMRs. In Asia, the current CMRs in Pakistan, Myanmar and India were the highest in their region and were 1 SD below the regional mean. Alongside South Korea, these nations had the lowest percentage reductions in CMRs. In Sub-Saharan Africa, the current CMRs in Somalia, Burkina Faso, Sierra Leone, Chad, Democratic Republic of Congo and Angola were the highest in their region and were 1 SD below the regional mean. CONCLUSIONS: Those concerned with the pursuit of social justice need to alert their societies to the corrosive impact of poverty on child mortality. Progress in reducing CMRs provides an indication of how well nations are meeting the needs of their children. Further country-specific research is required to explain regional differences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".