Relief and Rescue Operations during Mass Gatherings: A Systematic Review
Bibliographic record
Abstract
Large-scale events known as mass gatherings (MGs) present considerable difficulties for emergency management particularly when it comes to providing relief and rescue services.The possibility of mishaps terrorist strikes and public health crises give rise to these difficulties.Ensuring the safety and well-being of participants during such events requires effective management.To compile the most recent information and methods for relief and rescue efforts during MGs is the goal of this systematic review.It looks for practical approaches and draws attention to areas where research is lacking in order to suggest future lines of inquiry.Researchers used databases such as, PubMed, Scopus, Web of Science, and Google Scholar to perform a thorough search of the literature encompassing publications from 2000 to 2023.The terms "mass gatherings", "disaster preparedness", "public health", "emergency management", "relief operations", and "rescue operations" were among them.Inclusion criteria encompassed books, guidelines, and qualitative and quantitative studies on relief and rescue efforts during MGs.The data quality assessment was performed independently by multiple reviewers.The review included 52 sources from various regions, including Canada, Asia, Europe, and America.Significant improvements in the efficiency of relief and rescue operations are attributed to pre-event preparation and multiagency coordination.Developments in technology, including real-time data analysis and communication tools, improve situational awareness and resource allocation.Nevertheless, there are still issues to be resolved, such as unmet educational needs of staff, poor communication, and technical limitations.The review concludes by highlighting the significance of involving multiple agencies and incorporating public health considerations into emergency planning.Further, effective relief and rescue operations during MGs require combining advanced technology, and thorough planning.More qualitative and experimental studies from different geographical contexts can provide valuable insights into regionspecific challenges and solutions.This review underscores the critical need for continuous improvement and adaptation in emergency management strategies to safeguard public health and safety during MGs.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".