Private ownership of primary care providers associated with patient perceived quality of care
Bibliographic record
Abstract
Ownership of primary care providers varies in different cities in China. Shanghai represented the full public ownership model of primary providers; Shenzhen had public-owned but private-operated providers; and Hong Kong represented the full private ownership. The study aims to assess the association of primary care ownership and patient perceived quality of care in 3 Chinese megacities.We conducted multistage stratified random surveys in 2013 in the 3 cities. Quality scores of primary care were measured using the validated primary care assessment tools. Multivariate linear regression models were used to compare quality scores after controlling potential confounders of patient demographic, socioeconomic, and healthcare utilization factors.Overall, 797 primary care users in Shanghai, 802 in Shenzhen, and 1325 in Hong Kong participated in the study. The mean total quality scores were reported the highest in Shanghai (28.39), followed by Shenzhen (25.82) and then Hong Kong (25.21) (P < 0.001). Shanghai participants reported the highest scores for 1st contact accessibility, coordination of information, comprehensiveness of service availability, and culture competence, while Hong Kong participants reported the lowest for these domains (P < 0.001). Hong Kong participants from rich households reported higher total scores than those from poor households (P < 0.05); however, this was not found in Shanghai and Shenzhen.The study suggests that private primary care ownership may be associated with lower quality and less equitable care distribution. In China, it suggests that it may be beneficial to promote public-owned and nonprofit providers. Promoting privatization in primary care may be at the cost of quality and equity of primary care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".