운항 중 항공기 내 감염병 확산 방지를 위한 해외 주요 지침의 대응 요소 분석 연구 - 미국, 캐나다, 유럽연합, 호주, 중국의 객실 운영지침을 중심으로
Bibliographic record
Abstract
Purpose: As communicable disease, COVID-19, pandemic strikes over the world, it is critically bewared that air travel possibly be a major pass way to deliver the infectious disease virus. Especially the airplane could be an unique environment to cultivate the virus spreaders. In order to keep the continuous safe airway as well as the industry, related international associations and organizations have been published the guidelines for the prevention and control the infectious disease through the all aspects of aviation. By reviewing the guidelines, focusing on the in-flight infection prevention and control, this study would not only inform a summary of the international guidelines but also provide an essential and general consideration for related research or guideline study. Methods: Guidelines of 5 major countries are reviewed, which has been seriously influenced by COVID-19 : U.S., Canada, E.U., Australia and China. The items of the guidelines are re-categorized as its similarity and structure by applicable cases. Results: The result of this study shows that each guideline seems to share a major structure and issue such as identifying sick traveler, sick passenger care, and cleaning even though that of China has a different since it used to consider the flight conditions based on 3 levels of infection risk. For sick passenger care, the guidelines includes crew safety, service level, sick passenger isolation, and cleaning. Implications: A published guideline as a public manual could be to prevent and control the in-flight infection efficiently and promptly. It also could provide a confidence of knowledge and educate for all users to prepare the in-flight emergency as well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.013 |
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; both teacher heads agree on what is shown here.
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".