Bibliometric Analysis of Emergency Medicine in Disasters: 2004-2023
Bibliographic record
Abstract
Objective: This study aims to conduct a bibliometric analysis of research on "emergency medicine in disasters" published between 2004 and 2023, highlighting the scientific developments, key themes, and research gaps in this growing field.With the increasing frequency of disasters due to factors like climate change, urbanization, and population growth, the importance of disaster emergency medicine has become more critical.While technological advancements have improved emergency medical responses, research shows that further development is needed.This analysis seeks to evaluate global trends and collaborations in disaster medicine research to provide a strategic roadmap for future studies and enhance the preparedness and effectiveness of health systems in responding to disasters.Methods: A bibliometric review of 1,532 articles published between January 1, 2004 and December 31, 2023, was conducted using the Web of Science Core Collection database.The analysis focused on articles published in peer-reviewed journals, limited to the "emergency medicine" field, written in English, and meeting the defined timeframe.The selected articles were examined based on publication trends, citation counts, journal distribution, most-cited authors, and collaborative networks between institutions and countries.Tools such as keyword networks, bibliographic coupling, co-authorship analysis, and citation mapping were used to visualize research collaborations and thematic focus areas.VOSviewer software was employed to map research collaborations and identify the most influential studies in disaster medicine.Results: The study's findings reveal a significant increase in research output, particularly following global crises such as coronavirus disease-2019 (COVID-19).Countries like the United States of America (USA), Canada, the United Kingdom, and China lead in both publications and international collaborations, demonstrating strong partnerships in disaster medicine research.Institutions such as Harvard University and Johns Hopkins University stand out for their high productivity and impact, with highly cited articles focusing on disaster-related health impacts, triage, and the mental health of responders.Key research themes include disaster preparedness, emergency medical services, and global health crises, underscoring the growing importance of international collaboration in advancing disaster medicine. Conclusion:The bibliometric analysis of research on "emergency medicine in disasters" from 2004 to 2023 demonstrates a substantial increase in scientific output, especially following the COVID-19 pandemic.Key findings highlight the central role of journals like Prehospital and Disaster Medicine and the influential contributions of institutions such as Harvard University and Johns Hopkins University.Frequently cited articles focus on disaster health impacts, triage, and mental health support for healthcare workers, reflecting the critical importance of preparedness and response strategies.International collaborations, particularly among countries like the USA, Sweden, Iran, and Turkey, have expanded, underlining the growing global significance of disaster medicine.These results underscore the vital role that disaster emergency medicine plays in strengthening global health systems and the increasing academic focus on this field.
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 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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.183 | 0.252 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".