Evidence map›Paper›PMID 37873825›Full record

ArticleNursing reports (Pavia, Italy)2023

How Do Disaster Relief Nurses in Japan Perceive and Respond to Risks? A Cross-Sectional Study.

Aki Nishikawa, Takumi Yamaguchi, Yumiko Yamada, Hideko Urata, Tetsuko Shinkawa, Yuko Matsunari

Open access · goldAbstract read
In one paragraph

Article in Nursing reports (Pavia, Italy), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 27% of its field
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

3 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

6 authors at 3 institutions in 1 country.

Aki NishikawaNagasaki Rosai Hospital, Nagasaki 857-0134, Japan.
Takumi YamaguchiResearch Administration Center, Saitama Medical University, Saitama 350-0495, Japan.
Yumiko YamadaDepartment of Nursing, Kwassui Women's University, Nagasaki 856-0835, Japan.
Hideko UrataAtomic Bomb Disease Institute, Nagasaki University, Nagasaki 852-8523, Japan.
Tetsuko ShinkawaAtomic Bomb Disease Institute, Nagasaki University, Nagasaki 852-8523, Japan.
Yuko MatsunariSchool of Health Sciences, Kagoshima University, Kagoshima 890-8544, Japan.
Kagoshima University · JPNagasaki University · JPKwassui Women's University · JP

Funding

The Research and Education Center for Natural Hazards, Kagoshima University. NA
6 · The paper itself

Abstract

This study assessed the risk perceptions among disaster relief nurses (DRNs) in Japan by focusing on 15 risk factors associated with frequent natural disasters and the ongoing coronavirus disease 2019 (COVID-19) pandemic. We conducted a cross-sectional study that targeted DRNs across six prefectures in Japan and explored nurses' perceptions of risks including radiation exposure, volcanic eruptions, and mass infections. The findings indicated a heightened perception of radiation and nuclear-related risks. In the context of the COVID-19 pandemic, DRNs perceived "mass infection" as a significant risk. An age-based analysis revealed that younger nurses had more dread about "mass infection" and had heightened uncertainty about the "X-ray test" compared with their older peers. Understanding DRNs' risk perceptions is crucial for effective disaster response preparedness and training. The study highlights the need to address these perceptions to ensure that DRNs are well prepared and supported in their roles. This study was not pre-registered on a publicly accessible registry.

Indexed as

disaster nursingdisaster relief nursingrisk perception

Identifiers

PMID37873825
PMCPMC10594493
OpenAlexW4387450417

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.