A qualitative approach to investigate emergency preparedness state for the built environment in the UAE
Bibliographic record
Abstract
Purpose Emergency preparedness (EP) is one of the crucial phases of the disaster management cycle for the built environment. The body of knowledge, therefore, reports on different preparedness standards adopted by developed countries such as the United Kingdom (UK), the United States of America (USA), Canada, Japan and Australia. Other countries, however, such as the United Arab Emirates (UAE) (in the absence of its preparedness framework), have long adapted the UK preparedness standards. This has called for this study to investigate the state of EP practices in the UAE to identify the limitations and challenges it has been facing during its preparedness phase when adopting the UK preparedness standards. Design/methodology/approach Qualitative methods of data collection and documentation with the content analysis were adopted to identify the barriers faced by the preparedness phase of emergency management (EM) in the UAE. A Pilot study was therefore conducted to validate eight key elements of the EP phase identified from the literature. The state of EP phase and the extent to which the eight key elements of EP elements were practiced and the barriers in their implementation in the UAE were explored through interviews at federal (National Crisis and Emergency Management Authority) and local levels (local team of crisis and emergency management). Findings The study identified eight key elements of the EP phase and the associated barriers related to their implementation in the UAE. The barriers were ranked based on their severity by interviewing experts at both federal and local levels. Practical implications This paper addresses the need to investigate the state of the EP phase, its key elements and the barriers faced during its implementation in the UAE. Originality/value Due to the absence of any EP frameworks or systems in the UAE, this paper aims to validate the EP elements identified by adopting a qualitative approach.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".