Understanding “Internet Plus Healthcare” in China: Policy Text Analysis
Bibliographic record
Abstract
BACKGROUND: The combination of the internet and healthcare has excellent benefits and far-reaching positive effects in improving service efficiency and promoting social equity. The role of the "internet plus healthcare" (IPHC) has been recognized, especially during the COVID-19 pandemic. This new healthcare model is also familiar to people and shows a bright prospect. OBJECTIVE: This article seeks to accurately understand and fully grasp the characteristics of IPHC policies that can enlighten the formulation of future policies. METHODS: The content analysis method was used to analyze China's IPHC policies collected from the Beida Fabao database and several official websites. RESULTS: We found that the development of IPHC policy has gone through 4 stages and is currently entering a phase of rapid development. IPHC policymakers are primarily health administrative departments. In addition, policy instruments are classified into either supply, environment, or demand, and policy themes can be summarized into 4 categories: facilities, technology, service, and management. CONCLUSIONS: China's IPHC policy has good prospects from the perspective of development trends. The health administrative departments mainly lead the development of China's IPHC policy. It is suggested that these departments involve other stakeholders (ie, medical workers, medical industries, and technology sectors) in formulating policies. Policies prefer to use supply-based and environment-based policy instruments. The policy themes emphasize improving infrastructure construction and high-quality diagnostic and treatment services, strengthening the supporting role of information technology, and ensuring all stakeholders understand their responsibilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".