Different Models of Hospital–Community Health Centre Collaboration in Selected Cities in China: A Cross-Sectional Comparative Study
Bibliographic record
Abstract
OBJECTIVE: In recent years, in order to provide patients with seamless and integrated healthcare services, some models of collaboration between public hospitals and community health centres have been piloted in some cities in China. The main goals of this study were to assess the nature and characteristics of these collaboration models. METHODS: Three cases of three different collaboration models in three Chinese cities were selected to analyse using descriptive statistics, Pearson χ (2) and ordinal logistic regression. RESULTS: Results showed that the Direct Management Model in Wuhan exhibited better structure indicators than the other two models. Staff in the Direct Management Model had the highest satisfaction level (77.6%) with respect to patient referral. Communications between hospitals and community health centres and among care providers were generally inadequate. Publicity about hospital-community health centre collaboration was inadequate, resulting in low awareness among patients and even among health professionals. CONCLUSION: Results can inform health service delivery integration efforts in China and provide crucial information for the assessment of similar collaborations in other countries.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".