Improving average health and persisting health inequities--towards a justice and fairness platform for health policy making in Asia
Bibliographic record
Abstract
BACKGROUND: Following a period of rapid economic and social change across Asia in the 1980s and 1990s, there have been persisting reports of public sector health systems decline and worsening health inequities within countries. Many studies and analyses in the region have indicated that these inequities are socially determined, leading to questions regarding the adequacy of current health policy approaches towards addressing the challenge of persisting health inequities. METHODS: Utilizing published data from Demographic Health Surveys (DHS) and case studies and reviews on health inequity in the Asian region, this article aims to describe the existing patterns of inequity of health access both within and between countries, focusing on immunization, maternal health access, nutritional outcomes and child mortality, with a view to recommending health policy options for addressing these health inequities. We compare the gap in access and outcomes between the highest and the lowest wealth quintiles, as well as cross-reference these findings with case studies and surveys on health inequities in the region. RESULTS: In Asia, while in terms of aggregate health more of the poor are being reached, the reduction in the gap between social groups in some cases is stagnating, particularly for maternal health access and childhood stunting. Inequity gaps for immunization are persisting, and remain very wide in large population countries. For child mortality, more of the poor are surviving, although the rate of mortality decline is more rapid in higher than lower socio-economic groupings. CONCLUSIONS: Both a strategic shift towards public health critique of social and political policy and operational shifts in health management and practice will be required to attain improvements in distributive health in Asia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".