An international comparison analysis of reserve and supply system for emergency medical supplies between China, the United States, Australia, and Canada
Bibliographic record
Abstract
Coronavirus disease 19 (COVID-19) has become a pandemic around the world. With the explosive growth of confirmed cases, emergency medical supplies are facing global shortage, which restricts the treatment of seriously ill patients and protection of medical staff. Taking China, the United States, Australia, and Canada as examples, this study compares and analyzes the reserve and supply systems of emergency medical supplies and problems exposed in response to the COVID-19 epidemic. Some common problems were found, such as insufficient types and quantities of emergency medical supplies in reserve, insufficient emergency production capacity, and imperfect command mechanism for emergency supplies deployment and transportation. A sound reserve system of emergency medical supplies is the basis and guarantee for dealing with public health emergencies such as major outbreaks. Based on the comparison of systems and practical experience, countries around the world should further improve the reserve and supply system of emergency medical supplies, and improve the coordination and cooperation mechanism for emergency supplies for international public health emergencies, so as to cope with increasingly severe public health emergencies in the context of globalization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".