Determinants of Overall Satisfaction with Public Clinics in Rural China: Interpersonal Care Quality and Treatment Outcome
Bibliographic record
Abstract
The primary health care quality factors determining patient satisfaction will shape patient-centered health reform in China. While rural public clinics performed better than hospitals and private clinics in terms of patient perceived quality of primary care in China, there is little information about which quality care aspects drove patients’ satisfaction. Using a World Health Organization database on 1014 rural public clinic users from eight provinces in China, our multiple linear regression model estimated the association between patient perceived quality aspects, one treatment outcome, and overall primary health care satisfaction. Our results show that treatment outcome was the strongest predictor of overall satisfaction (β = 0.338 (95% CI: 0.284 to 0.392); p < 0.001), followed by two interpersonal care quality aspects, Dignity (being treated respectfully) (β = 0.219 (95% CI: 0.117 to 0.320); p < 0.001) and Communication (clear explanation by the physician) (β = 0.103 (95% CI: 0.003 to 0.203); p = 0.043). Prompt attention (waiting time before seeing the doctor) and Confidentiality (talking privately to the provider) were not correlated with overall satisfaction. The treatment outcome focus, and weak interpersonal primary care aspects, in overall patient satisfaction, pose barriers towards a patient-centered transformation of China’s primary care rural clinics, but support the focus of improving the clinical competency of rural primary care workers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".