Understanding “Internet Plus Healthcare” in China: Policy Text Analysis (Preprint)
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 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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".