Public Hospitals in China: Is There a Variation in Patient Experience with Inpatient Care
Bibliographic record
Abstract
In China, public hospitals are the main provider of inpatient service. The Chinese public hospital reform has recently shifted towards health care organizations and delivery to improve health care quality. This study analyzes the variation of one of the dimensions of health care quality, patient-centeredness, among inpatients with different socioeconomic status and geographical residency in China. 1471 respondents who received inpatient care in public hospitals were included in our analysis. Patient-centeredness performance was assessed on the dimensions of Communication, Autonomy, Dignity, and Confidentiality. Variations of inpatient experience were estimated using binary logistic regression models according to: residency, region, age, gender, education, income quintile, self-rated health, and number of hospital admissions. Our results indicate that older patients, and patients living in rural areas and Eastern China are more likely to report positive experience of their public hospital stay according to the care aspects of Dignity, Communication, Confidentiality and Autonomy. However, there remains a gap between China and other countries in relation to inpatient experience. Noticeable disparities in inpatient experience also persist between different geographical regions in China. These variations of patient experience pose a challenge that China's health policy makers would need to consider in their future reform efforts.
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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 | 0.002 |
| 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".