Comparison of perceived quality amongst migrant and local patients using primary health care delivered by community health centres in Shenzhen, China
Bibliographic record
Abstract
BACKGROUND: Providing good quality primary health care to all inhabitants is one of the Chinese Government's health care objectives. However, information is scarce regarding the difference in quality of primary health care delivered to migrants and local residents respectively. This study aimed to compare patients' perceptions of quality of primary health care between migrants and local patients, and their willingness to use and recommend primary health care to others. METHODS: A cross-sectional survey was conducted. 787 patients in total were chosen from four randomly drawn Community Health Centers (CHCs) for interviews. RESULTS: Local residents scored higher than migrants in terms of their satisfaction with types of drugs available (3.62 vs. 3.45, p=0.035), attitude of health workers (4.41 vs. 4.14, p=0.042) and waiting time (4.30 vs. 3.86, p<0.001). Even though there was no significant difference in overall satisfaction between local residents and migrants (4.16 vs. 3.91, p=0.159), migrants were more likely to utilize primary health care as the first choice for their usual health problems (94.1% vs. 87.1%, p=0.032), while local residents were more inclined to recommend Traditional Chinese Medicine to others (65.6% vs. 56.6%, p=0.026). CONCLUSIONS: Quality of primary health care given to migrants is less satisfactory than to local residents in terms of attitude of health workers and waiting time. Our study suggests quality of care could be improved through extending opening hours of CHCs and strengthening professional ethics education. Considering CHCs as the first choice by migrants might be due to their health insurance scheme, while locals' recommendations for traditional Chinese medicine were possibly because of cultural differences.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".