Emergency Medical Services Preparedness and Risk Mitigation in Mass Gathering Health Security Contexts- An Updated Review for Healthcare Security
Bibliographic record
Abstract
Background: Mass gathering events pose complex health security challenges due to high population density, diverse risk exposures, and the potential to overwhelm local emergency medical services (EMS). Aim: This review aims to examine contemporary strategies for emergency medical services preparedness and risk mitigation in mass gathering health security contexts. Methods: A narrative review approach was employed, synthesizing international guidance, EMS operational frameworks, and evidence derived from prior mass gathering events, with emphasis on preparedness, staffing, resource allocation, and incident command integration. Results: Findings indicate that effective mass gathering healthcare depends on comprehensive risk assessment, multidisciplinary planning, scalable medical staffing, environmental risk mitigation, and robust communication systems. Onsite medical care, physician oversight, and structured use of the Incident Command System substantially reduce unnecessary hospital transports and preserve community healthcare capacity. Conclusion: Proactive, systemsbased medical planning enhances patient safety, EMS efficiency, and overall community resilience during mass gathering events.
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 | 0.033 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".