ArticleJournal of education and health promotion2022
Analysis of the intellectual and social structure of health system response plan to earthquake studies adopting word co-occurrence and narrative approaches.
Article in Journal of education and health promotion, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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Authors and funding
5 authors.
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Abstract
BACKGROUND AND
aimEarthquake is a natural disaster severely affecting the societies' health, hygiene, and welfare as such the most effective method to respond to its damages is to develop a readiness plan. This study aimed to discover thematic patterns and co-author relationships extracted from relevant publications to plan an earthquake response. MATERIALS AND
methodsThis descriptive-analytical study adopted the scientometric approach and used word co-occurrence and social network analysis. The published articles indexed in PubMed were retrieved from 1970 to 2021 using a combination of keywords "earthquake and response." The data were analyzed in VOSviewer, UCINET, and NetDraw software.
resultsThe following six thematic clusters with a social map were extracted: Initial response of the healthcare system, response to probabilistic risks after the hazard, response to mental health and community resilience, response to public health, response to post-traumatic stress disorders, and staff's response to the needs assessment and continuity of diagnostic treatment services. Moreover, social network analysis revealed the great impact of Japanese and Chinese authors and institutes.
conclusionThe present study detected many gaps in the literature on earthquake response, which can contribute to developing a general framework to prepare integrated healthcare earthquake response plans to promote the performance of this system.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.