Political and Welfare State Determinants of Infant and Children’s Health Indicators:An Analysis of Wealthy Countries
Bibliographic record
Abstract
Economic indicators such as income inequality are gaining attention as putative determinants of population health. On the other hand, we are just beginning to explore the health impact on population health of political and welfare state variables such as political orientation of government or type of medical care coverage. To determine the socially structured impact of political and welfare state variables on low birth weight rate, infant mortality rate, and under-five mortality rate, we conducted an ecological study with un balanced time-series data from 19 wealthy OECD countries for the years from 1960 to 1994. Among the political/welfare state variables, total public medical coverage was the most significant predictor of the mortality outcomes. The low birth weight rate was more sensitive to political predictors such as percentage of vote obtained by social democratic or labor parties. Overall, political and welfare state variables (including indicators of health policies) are associated with infant and child health indicators. While a strong medical care system seems crucial to some population health outcomes (e.g., the infant mortality rate), other population health outcomes might be impacted by social policies enacted by parties supporting strong welfare states (the low birth weight rate). Our investigation suggests that strong political will that advocates for more egalitarian welfare policies, including public medical services, is important in maintaining and improving the nation’s health. © 2006 Elsevier Ltd. All rights reserved.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".