Adaptation and Promotion of Emergency Medical Service Transportation for Climate Change
Bibliographic record
Abstract
The purpose of this study is to find a proper prehospital transportation scenario planning of an emergency medical service (EMS) system for possible burdensome casualties resulting from extreme climate events. This project focuses on one of the worst natural catastrophic events in Taiwan, the 88 Wind-caused Disasters, caused by the Typhoon Morakot; the case of the EMS transportation in the Xiaolin village is reviewed and analyzed. The sequential-conveyance method is designed to promote the efficiency of all the ambulance services related to transportation time and distance. Initially, a proposed mobile emergency medical center (MEMC) is constructed in a safe location near the area of the disaster. The ambulances are classified into 2 categories: the first-line ambulances, which reciprocate between the MEMC and the disaster area to save time and shorten the working distances and the second-line ambulances, which transfer patients in critical condition from the MEMC to the requested hospitals for further treatment. According to the results, the sequential-conveyance method is more efficient than the conventional method for EMS transportation in a mass-casualty incident (MCI). This method improves the time efficiency by 52.15% and the distance efficiency by 56.02%. This case study concentrates on Xiaolin, a mountain village, which was heavily destroyed by a devastating mudslide during the Typhoon Morakot. The sequential-conveyance method for the EMS transportation in this research is not only more advantageous but also more rational in adaptation to climate change. Therefore, the findings are also important to all the decision-making with respect to a promoted EMS transportation, especially in an MCI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".