Bridging the Gaps Between Patients and Primary Care in China: A Nationwide Representative Survey
Bibliographic record
Abstract
PURPOSE: China introduced a national policy of developing primary care in 2009, establishing 8,669 community health centers (CHCs) by 2014 that employed more than 300,000 staff. These facilities have been underused, however, because of public mistrust of physicians and overreliance on specialist care. METHODS: We selected a stratified random sample of CHCs throughout China based on geographic distribution and urban-suburban ratios between September and December 2015. Two questionnaires, 1 for lead clinicians and 1 for primary care practitioners (PCPs), asked about the demographics of the clinic and its clinical and educational activities. Responses were obtained from 158 lead clinicians in CHCs and 3,580 PCPs (response rates of 84% and 86%, respectively). RESULTS: CHCs employed a median of 8 physicians and 13 nurses, but only one-half of physicians were registered as PCPs, and few nurses had training specifically for primary care. Although virtually all clinics were equipped with stethoscopes (98%) and sphygmomanometers (97%), only 43% had ophthalmoscopes and 64% had facilities for gynecologic examination. Clinical care was selectively skewed toward certain chronic diseases. Physicians saw a median of 12.5 patients per day. Multivariate analysis showed that more patients were seen daily by physicians in CHCs organized by private hospitals and those having pharmacists and nurses. CONCLUSIONS: Our survey confirms China's success in establishing a large, mostly young primary care workforce and providing ongoing professional training. Facilities are basic, however, with few clinics providing the comprehensive primary care required for a wide range of common physical and mental conditions. Use of CHCs by patients remains low.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".