Evidence map›Paper›PMID 34778161›Full record

ArticleFrontiers in public health2021

How Can Nursing Teams Respond to Large-Scale COVID-19 Screening?

Huihan Zhao, Yu He, Fang Brister, Li Yang, Gaoye Li, Ying Ling, Yanping Ying

Abstract read
In one paragraph

Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Huihan ZhaoDepartment of Nursing, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Yu HeDepartment of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Fang BristerClinical Research Unit, UT Southwestern Medical Center, Dallas, TX, United States.
Li YangDepartment of Nursing, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Gaoye LiDepartment of Nursing, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Ying LingDepartment of Nursing, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Yanping YingDepartment of Nursing, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 virus has devastated lives and economies worldwide. The responses of nursing teams to large-scale COVID-19 screening have rarely been addressed or described. The aim of this study is to introduce an efficient response strategy for nurses in large-scale COVID-19 screening. A new COVID-19 case was confirmed on Jan 14, 2021 in Nanning, China. Immediately, a large-scale COVID-19 screening was launched and ran from Jan 14 to Jan 17, 2021. Our nurse team responding to the screening included three major components: (1) establishing a leadership group and a nucleic acid sampling emergency team; (2) defining, conducting, and evaluating nurse training; (3) implementing efficient sampling schemes (10 in 1 mixed sample technique). A total of 500 nurse volunteers were recruited and divided into three echelons. A total of 353 trained nurses were sent to 65 sampling stand stations. In cooperation with nurses from other health institutions, samples were collected from a total of 854,215 people in only 4 days for 2019-nCOV nucleic acid screening. The preparation and efficient response strategies used to conduct this screening may provide a baseline reference for future large-scale COVID-19 screening worldwide.

Indexed as

COVID-19EmergenciesHumansLeadershipNursing, TeamSARS-CoV-210 in 1 testCOVID-19large-scalenucleic acid screeningnurse

Identifiers

PMID34778161
PMCPMC8585840

What OpenQuestion holds

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LicenceCC BY
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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.