Comparative Analyses of Institutions,Regulations and Administration on Healthcare Risk Management in United Kingdom,United States,Canada,Australia and Taiwan District
Bibliographic record
Abstract
Objective Interpretation of the growing body of global literature on health care risk is compromised by a lack of common understanding and language.This series of articles aims to comprehensively compare laws and regulations,institutional management,and administration of incidence reporting systems on medical risk management in the United Kingdom,the United States,Canada,Australia,and Taiwan,so as to provide evidence and recommendations for health care risk management policy in China.Methods We searched the official websites of the healthcare risk management agencies of the four countries and one district for laws,regulatory documents,research reports,reviews and evaluation forms concerned with healthcare risk management and assessment.Descriptive comparative analysis was performed on relevant documents.Results A total of 146 documents were included in this study,including 2 laws(1.4%),17 policy documents(11.6%),41 guidance documents(28.1%),37 reviews(25.3%),and 49 documents giving general information(33.6%).The United States government implemented one law and one rule of patient safety management,while the United Kingdom and Australia each issued professional guidances on patient safety improvement.The four countries implemented patient safety management policy on four different levels: national,state/province/district,hospital,and non-governmental organization.Conclusion The four countries and one district adopted four levels of patient safety management,and the administration modes can be divided into an NGO-led mode represented by the United States and Canada and a government-led mode represented by the United Kingdom,Australia,and Taiwan.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".