Emergency Preparedness within the Hotel Industry: A Case Study of Wuhan City, China
Bibliographic record
Abstract
The hospitality industry is one of the most vulnerable to emergency events and can be highly affected by various hazards. Within the hospitality industry, hotels are faced with multi-crises and disasters. Emergency preparedness is one of the most efficient ways to deal with emergencies and increase the resilience of the businesses, especially in high-risk areas. This research aims to identify the main risks that may threaten hotels and investigate the state of emergency preparedness of “five-star” hotels in Wuhan city, China, by conducting interviews among general manager and sending questionnaires to safety and security managers in the hotel sectors. Findings show that Wuhan’s hotels are exposed to a wide range of risks, and all of the respondent five-star hotels have plans or frameworks prepared for crises and disasters. Results indicate that there is an overall high level of preparedness for crises and disasters in five-star hotels in Wuhan, China. Findings also reveal that many of the challenges facing hoteliers are connected to emergency communication and tourist-oriented disaster preparedness planning. On the basis of the results, implications are discussed. It is hoped that this paper will shed light on emergency preparedness for hotel industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".