What structural factors influencing emergency and disaster medical response teams? A comparative review study
Bibliographic record
Abstract
INTRODUCTION: An important indicator increasing the survival chances of patients and injured people immediately after emergency and disaster is the rapid access to medical services. The establishment of Emergency and Disaster Medical Response Teams (EDMRTs) is one of the main strategies to improve response capacity and capability in the field of EDMRT. This study aimed to probe the structural factors influencing of EDMRTs. METHODOLOGY: In this study, a comparative review method was used. The current study was conducted between March 2017 and September 2018. For this, articles, books, formal reports, and information concerning the available websites regarding the structure of EDMRTs were analyzed. To access relevant scientific articles, an extensive search was carried out in several steps using divergent keywords in the Scopus, ProQuest, PubMed, ScienceDirect, and Google Scholar databases. After accessing the resources and documents, the process of analyzing and comparing different team structures was carried out using content analysis. RESULTS: Following the search of relevant databases and websites, the structure of EDMRTs in the United States, Australia, Japan, Turkey, New Zealand, Canada, and the World Health Organization were taken into consideration and compared. Two areas of "Organization and Management" as well as "Capacity and Capability Development" were explored along with multiple subsets. CONCLUSION: The results of this study revealed that the model and structure of EDMRTs have direct relationship with such elements as the structure of the disaster risk management system, risk assessment, impact of the hazards and medical needs of the affected area, population distribution, level of team activity, and timing of the teams' presence after disasters. The research team recommends designing and conducting studies for determine the roles and responsibilities of the teams.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".