Outpatient prescription practices in rural township health centers in Sichuan Province, China
Bibliographic record
Abstract
BACKGROUND: Sichuan Province is an agricultural and economically developing province in western China. To understand practices of prescribing medications for outpatients in rural township health centers is important for the development of the rural medical and health services in this province and western China. METHODS: This is an observational study based on data from the 4th National Health Services Survey of China. A total of 3,059 prescriptions from 30 township health centers in Sichuan Province were collected and analyzed. Seven indicators were employed in the analyses to characterize the prescription practices. They are disease distribution, average cost per encounter, number of medications per encounter, percentage of encounters with antibiotics, percentage of encounters with glucocorticoids, percentage of encounters with combined glucocorticoids and antibiotics, and percentage of encounters with injections. RESULTS: The average medication cost per encounter was 16.30 Yuan ($2.59). About 60% of the prescriptions contained Chinese patent medicine (CPM), and almost all prescriptions (98.07%) contained western medicine. 85.18% of the prescriptions contained antibiotics, of which, 24.98% contained two or more types of antibiotics; the percentage of prescriptions with glucocorticoids was 19.99%; the percentage of prescriptions with both glucocorticoids and antibiotics was 16.67%; 51.40% of the prescriptions included injections, of which, 39.90% included two or more injections. CONCLUSIONS: The findings from this study demonstrated irrational medication uses of antibiotics, glucocorticoids and injections prescribed for outpatients in the rural township health centers in Sichuan Province. The reasons for irrational medication uses are not only solely due to the pursuit of maximizing benefits in the township health centers, but also more likely attributable to the lack of medical knowledge of rational medication uses among rural doctors and the lack of medical devices for disease diagnosis in those township health centers. The policy implication from this study is to enhance professional training in rational medication uses for rural doctors, improve hardware facilities for township health centers, promote health education to rural residents and establish a public reporting system to monitor prescription practices in rural township health centers, etc.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".