What are the similarities and differences in structure and function among the three main models of community health centers in China: a systematic review
Bibliographic record
Abstract
BACKGROUND: There are three major models of primary care providers (Community Health Centers, CHCs) in China, i.e., government managed, hospital managed and privately owned CHCs. We performed a systematic review of structures and health care delivery patterns of the three models of CHCs. METHODS: Studies from relevant English and Chinese databases for the period of 1997-2011 were searched. Two independent researchers extracted data from the eligible studies using a standardized abstraction form. Methodological quality of included articles was assessed with the Mixed Methods Appraisal Tool (MMAT). RESULTS: A total of 13 studies was included in the final analysis. Compared with the other two models, private CHCs had a smaller health workforce and lower share of government funding in their total revenues. Private CHCs also had fewer training opportunities, were less recognized by health insurance schemes and tended to provide primary care services of poor quality. Hospital managed CHCs attracted patients through their higher quality of clinical care, while private CHCs attracted users through convenience and medical equipment. CONCLUSIONS: Our study suggested that government and hospital managed CHCs were more competent and provided better primary care than privately owned CHCs. Further studies are warranted to comprehensively compare performances among different models of CHCs.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".