Collaboration of issuing agencies and topic evolution of health informatisation policies in China
Bibliographic record
Abstract
Digital transformation in the Chinese healthcare industry has led national government agencies to issue a series of policies to guide the construction of health informatisation. However, little is known about the issuing agencies and the topics of health informatisation policies. This study aimed to explore the collaboration of policies issuing and the evolution of policy topics. In this study, a total of 156 policy documents were identified. Author–Topic model and pre-discretised method based on Latent Dirichlet Allocation model were employed to mine the correlation between the issuing agencies and policy topics, and the evolution of policy contents. Findings suggest that the development of health informatisation policies can be divided into three stages. The number of policies has been increasing constantly, among which the policy of opinion and notification accounts for the vast majority. Many government agencies are involved in formulating policies collaboratively. On the whole, the topics changed constantly over time. From 2003 to 2008, policy topics focused on standards and specifications, with the phenomenon of splitting and development. From 2009 to 2014, policies were predominantly related to the construction of regional health informatisation, with some emerging topics generating. Internet + medical and new information technology gained attention from 2015 to 2020; most topics in this period were inherited, split or merged from the previous period. This study is helpful to research and formulation of the health informatisation-related policies.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".