Evidence map›Paper›PMID 35854380›Full record

ArticleBMC nursing2022

The immediate and short-term impact of COVID 19 infections on nurses in a UAE hospital.

Khaled Al Qawasmeh, Nabeel Al Amiri, Salma Omar Al Nuaimi

Abstract read
In one paragraph

Article in BMC nursing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Khaled Al QawasmehDepartment of Nursing, Tawam Hospital, Post Box 15258, Al Ain, UAE. khaled62@hotmail.com.ORCID http://orcid.org/0000-0002-8965-6529
Nabeel Al AmiriDepartment of Nursing, Tawam Hospital, Post Box 15258, Al Ain, UAE.ORCID http://orcid.org/0000-0003-4446-1637
Salma Omar Al NuaimiDepartment of Nursing, Tawam Hospital, Post Box 15258, Al Ain, UAE.ORCID http://orcid.org/0000-0002-1415-2502

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

aimsNurses were on the front line against the COVID 19 pandemic, fighting to save human lives. Many nurses sacrificed their well-being and social life to win the battle. In consequence, many nurses have been infected with the virus around the Globe. This study aims to determine the immediate and short-term physical, psychological, and social impact of COVID 19 infection on nurses and midwives retrospectively. On the other hand, it aims to find the effect of COVID 19 stigma on the self-esteem of the infected nurses.

methodsTo achieve this goal, the authors used an exploratory, mixed-method design with a sample of nurses and midwives working in a tertiary hospital in UAE who has been infected with the COVID 19 virus and recovered. The authors have used the qualitative results to explain and interpret the findings of the quantitative findings. The data were collected through distributing the quantitative survey to participants and then it was followed by conducting semi-structured interviews.

resultsThe careful exploration of the experiences of nurses infected with the COVID 19 virus suggested a simple model that manages the patients in hospitals and at homes, including improving self-efficacy and patient coping, providing the basic nursing skills to patients and families, providing continuous psychological support, and providing high standards of health care.

conclusionThe new suggested model will maintain a positive status of wellbeing amongst infected patients during the infection time and in 3-6 months after the infection.

Indexed as

COVID 19COVID 19 stigmaNursesPhysicalPsychologicalSelf-esteemSocialUAE

Identifiers

PMID35854380
PMCPMC9295355

What OpenQuestion holds

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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.