Evidence map›Paper›PMID 34392610›Full record

ArticleNursing open2021

Late-onset PTSD and coping strategies for frontline nurses during the COVID-19 epidemic in China.

Yongfang Jiang, Bowen Hu, Baoren Tu, Quan Zhuang

Open access · goldAbstract read
In one paragraph

Article in Nursing open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 14% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
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  5. 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

4 authors at 1 institution in 1 country.

Yongfang JiangTransplantation Center, The 3rd Xiangya Hospital, Central South University, Changsha, China.
Bowen HuXiangya School of Medicine, Central South University, Changsha, China.
Baoren TuTransplantation Center, The 3rd Xiangya Hospital, Central South University, Changsha, China.
Quan ZhuangTransplantation Center, The 3rd Xiangya Hospital, Central South University, Changsha, China.ORCID https://orcid.org/0000-0001-5225-9282
Central South University · CN

Funding

Hunan Provincial Natural Science Foundation (2020JJ3058).National Natural Science Foundation of China 81700658
6 · The paper itself

Abstract

aimTo evaluate the late-onset post-traumatic stress disorder (PTSD) situation, coping strategies and social supports for frontline clinical nurses 6 months after involvement in fighting against coronavirus disease 2019 (COVID-19) outbreak in China.

designA cross-sectional design.

methodsWe recently randomly distributed a questionnaire online to Chinese nurses who had taken care of COVID-19 patients since the end of January 2020. The questionnaire was made up of three professional scales, the impact of event scale-revised, simplified coping style questionnaire and social support rating scale included.

resultsThe general prevalence of late-onset PTSD among frontline nurses was 88.19%. Nurses who worked in Hubei Province (the kindle place of the COVID-19 outbreak) showed lower risk of late-onset PTSD symptoms than those who did not. We also found that positive coping strategies were correlated with less late-onset PTSD symptoms. Meanwhile, getting more social supports could help these nurses to obtain positive coping strategies.

Indexed as

COVID-19EpidemicsNursesStress Disorders, Post-TraumaticAdaptation, PsychologicalChinaCross-Sectional StudiesHumansSARS-CoV-2coping strategyCOVID-19frontline nurselate-onset PTSDsocial support

Identifiers

PMID34392610
PMCPMC8441903
OpenAlexW3195703639

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.