Evidence map›Paper›PMID 36845039›Full record

ArticleHeliyon2023

Nurses' adaptations to changes on a COVID-19 ward in South Korea: A qualitative study.

Won Lee, Jeehee Pyo, Minsu Ock, Seung Gyeong Jang, Eun Young Choi

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  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

5 authors.

Won LeeDepartment of Nursing, Chung-Ang University, Seoul, Republic of Korea.
Jeehee PyoTask Forces to Support Public Health and Medical Services in Ulsan Metropolitan City, Ulsan, Republic of Korea.
Minsu OckDepartment of Preventive Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea.
Seung Gyeong JangCollege of Nursing, Inje University, Gimhae, Republic of Korea.
Eun Young ChoiDepartment of Nursing, Chung-Ang University, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Elucidating nurses' adaptation to changes in the early stage of an infectious disease epidemic is necessary to promote nurses' coping with and adapting to situations in which new infectious diseases are predicted periodically. Aim: To explore nurses' adaptation to changes in COVID-19 wards in South Korea. Methods: In-depth interviews were conducted with 20 nurses through purposive sampling from May to August 2020. The collected data were transcribed verbatim, and analysis was performed using conventional content analysis. Findings: Three categories emerged from the interviews: (a) Disruption caused by an unforeseen pandemic outbreak, (b) perseverance through the turmoil of changes as a nurse, and (c) transition from feelings of fearfulness to those of accomplishment. While the nurses initially struggled to care for patients with COVID-19, they made conscious efforts to provide emotional nursing and maintain their professionalism. Discussion: Nurses caring for patients with COVID-19 have faced a number of challenges but have adapted to new scenarios by endeavoring to fulfil their professional roles. Conclusion: To overcome a national disaster situation such as COVID-19, the government and healthcare organizations should prepare strategies to support the efforts of nurses to strengthen their own professionalism.

Indexed as

COVID-19ExperienceNursingQualitative researchSouth Korea

Identifiers

PMID36845039
PMCPMC9941071

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.