Health status and health disparity in China: a demographic and socioeconomic perspective
Bibliographic record
Abstract
Using Chinese General Social Survey (CGSS) in 2005, 2008 and 2013, this study investigates health determinants and health inequality in China. The ordinal complementary log–log model is used firstly to examine the impact of individual and contextual factors on self-rated health status. The study further checks the health inequality among subgroups divided by health determinants considered in the determinant model. We find that there are significant gender, residential, ethnic, socioeconomic, emotional, regional, and periodic differences. Moreover, the health status of sub-groups defined by factors used in this research is affected by health determinants in different ways which indicates the impact of these health determinants on health is moderated by each other. We conclude that while the health status generally varies with individual factors and social contexts, each group characterized by individual and contextual features has its own unique needs to improve and maintain their health status in China. The public policies aiming to increase Chinese health status and reduce health inequality must pay close attention to these needs while equalizing the availability, accessibility, and affordability of health facilities and health care system.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".