The work by the developing primary care team in China: a survey in two cities
Bibliographic record
Abstract
BACKGROUND: China is in the process of converting its existing primary care resources into general practice. The infrastructure is different from that of many other countries. OBJECTIVES: We surveyed patients' reasons for encounter (RFE) and the health providers' diagnoses in the general practice clinics of two large northern cities in order to assess the nature of the work of these practices. METHOD: Practices whose staff had a short course of training in the theory and practice of the International Classification of Primary Care (ICPC) were recruited to document the RFE and diagnoses of patient encounters in two separate winter weeks. RESULTS: The practices dealt mainly with chronic illness in older patients. Hypertension-related problems were the most frequent diagnoses, followed by upper respiratory tract infection. Patients also consulted very frequently for dizziness. Overall, there was good agreement between RFE and diagnosis in some organ systems. CONCLUSION: In their present form, the Chinese practices surveyed were delivering the full range of general practice care to a self-selected age group of patients. The ICPC was very useful for monitoring the work of general practice from the perspective of both the patients and the providers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".