Income-related children’s health inequality and health achievement in China
Bibliographic record
Abstract
INTRODUCTION: This study assessed income-related health inequality and health achievement in children in China, and additionally, examined province-level variations in health achievement. METHODS: Longitudinal data on 19,801 children under 18 years of age were derived from the China Health and Nutrition Survey. Income-related health inequality and health achievement were measured by the Health Concentration and Health Achievement Indices, respectively. Panel data with a fixed effect multiple regression model was employed to examine province-level variations in health achievement. RESULTS: A growing trend was towards greater health inequality among Chinese children over the last two decades. Although health achievement was getting better over time, the pro-rich inequality component has lessened the associated gain in achievement. Health achievement was positively impacted by middle school enrollments, the urbanization rate, inflation-adjusted per capita gross domestic product, and per capita public health spending. CONCLUSION: This study has provided evidence that average health status of Chinese children has improved, but inequality has widened. Widening inequality slowed the growth in health achievement for children over time. There were wide variations in health achievement throughout China.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".