A study on the equality and benefit of China’s national health care system
Bibliographic record
Abstract
BACKGROUND: This study is designed to evaluate whether the benefit which the residents received from the national health care system is equal in China. The perceived equality and benefit are used to measure the personal status of health care system, health status. This study examines variations in perceived equality and benefit of the national health care system between urban and rural residents from five cities of China and assessed their determinants. METHODS: One thousand one hundred ninty eight residents were selected from a random survey among five nationally representative cities. The research characterizes perceptions into four population groupings based on a binary assessment of survey scores: high equality & high benefit; low equality & low benefit; high equality & low benefit; and low equality & high benefit. RESULTS: The distribution of the four groups above is 30.4%, 43.0%, 4.6% and 22.0%, respectively. Meanwhile, the type of health insurance, educational background, occupation, geographic regions, changes in health status and other factors have significant impacts on perceived equality and benefit derived from the health care system. CONCLUSION: The findings demonstrate wide variations in perceptions of equality and benefit between urban and rural residents and across population characteristics, leading to a perceived lack of fairness in benefits and accessibility. Opportunities exist for policy interventions that are targeted to eliminate perceived differences and promote greater equality in access to health care.
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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".