Evidence map›Paper›PMID 38873663›Full record

ArticleHeliyon2024

How to improve smart emergency preparedness for natural disasters? ---- Evidence from the experience of ten pilot provinces in China for smart emergency.

Wei Liu, Jiayu Qian, Songjiang Wu

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Wei LiuCollege of Public Administration and Law, Hunan Agricultural University, Changsha, China.
Jiayu QianCollege of Public Administration and Law, Hunan Agricultural University, Changsha, China.
Songjiang WuCollege of Public Administration and Law, Hunan Agricultural University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The natural disasters faced by modern urban systems are complex, with multiple disaster-causing factors coexisting and secondary disasters occurring concurrently. With emergency management moving towards smart, natural disaster response has shifted from emergency-centered response to pre-disaster prevention. How to improve the government's natural disaster emergency preparedness has become an important issue that needs to be addressed. Based on the TOE (Technology-Organization-Environment) framework, the fsQCA method was used to explore the improvement path of emergency preparedness capacity of 10 pilot units in China to deal with natural disasters in 2020. Analyze the group effects and interrelationships of technology level, simultaneous supporting facilities, organizational construction, financial investment, external pressure, and social repercussions. The results show that: there exist four conditional groupings of high emergency preparedness in two modes. Two modes are organization-environment dual-drive and technology-organization-environment triple-drive, which have multiple concurrencies and follow the principle of consistent results. There are substitution effects in the conditional groupings of high emergency preparedness. There are causal asymmetries in the conditional groupings of high emergency preparedness and non-high emergency preparedness. This study aims to explore the smart emergency preparedness of ten pilot and to provide ideas for the overall development of "smart emergency response" and the improvement of emergency preparedness for natural disasters.

Indexed as

Emergency preparednessEmergency preparedness capabilitiesNatural disastersQualitative comparative analysisSmart emergency

Identifiers

PMID38873663
PMCPMC11170169

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.