A bibliometric analysis of health-related literature on natural disasters from 1900 to 2017
Bibliographic record
Abstract
BACKGROUND: Worldwide, natural disasters have caused a large number of deaths and considerable morbidity. Nevertheless, limited information is available on how the health-related literature on natural disasters has evolved. The current study aims to assess the growth and pattern of health-related literature on natural disasters. METHOD: A bibliometric method was implemented using Scopus database for the period from 1900 to 2017. Keywords used in the search strategy were obtained from the classifications of natural disasters presented by the Centre for Research on the Epidemiology of Disasters. The health component was determined by selecting the health-related subject areas in Scopus. RESULTS: In total, 9073 documents were retrieved. The annual number of publications showed a noticeable sharp increase after 2004. The retrieved documents received 97,605 citations, an average of 10.8 per document. The h-index of the retrieved documents was 113. Author keywords with the highest occurrence were 'earthquakes' followed by 'disaster medicine', 'disaster planning', 'tsunami', 'mental health', 'disaster preparedness', 'PTSD', 'emergency preparedness', and 'public health'. Authors from the United States of America contributed to 3127 (34.5%) publications and ranked first, followed by those from Japan (700; 7.7%) and China (636; 7.0%). When research output was standardised by Gross Domestic Product per capita, India ranked first, followed by China and the United States. The United Kingdom had the highest percentage of documents with international authors, followed by those from Switzerland and Canada. The Prehospital and Disaster Medicine journal published the most articles (636; 7.0%). The Sichuan University and its affiliated hospital contributed to 384 (7.0%) documents and ranked first in the field. CONCLUSION: The current baseline information on health-related literature on natural disasters showed that this field is growing rapidly but with inadequate international research collaboration. Research collaboration in this field needs to be strengthened to improve the global response to natural disasters in any place in the world. There is a need to expand the research focus in this field to include communicable and non-communicable diseases. Finally, the health effects of other natural disasters, such as floods, droughts and disease outbreaks, need to be addressed.
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.247 | 0.253 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".