Evidence map›Paper›PMID 39185505›Full record

ArticleSAGE open nursing

Measures to Prevent and Reduce Healthcare Worker Burnout During the COVID-19 Pandemic: A Scoping Review.

Patcharin Chaisurin, Natthawut Yodchai

Abstract readScoping Review
In one paragraph

Article in SAGE open nursing. 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. Review
  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

2 authors.

Patcharin ChaisurinNursing Division, Faculty of Nursing, Chiang Mai University, Chiang Mai, Thailand.ORCID https://orcid.org/0000-0001-6463-588X
Natthawut YodchaiInterdisciplinary Studies College, Payup University, Chiang Mai, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: In the COVID-19 era, burnout is a major occupational hazard among healthcare workers. This scoping review intended to investigate the pertinent literature concerning COVID-19 burnout among healthcare workers (HCWs) and the measures to prevent and reduce HCW burnout during the COVID-19 pandemic. Methods: The databases CINAHL, PubMed, ScienceDirect, and Scopus were systematically searched and screened for relevant papers. Additionally, manual searching was employed to supplement the electronic database results. The researchers examined 21 publications to answer the research question, "What have been the measures to prevent and reduce healthcare worker burnout during the COVID-19 pandemic?" The PRISMA 2020 checklist was used to guide the reporting of this scoping review. Results: It was found that to diminish healthcare workers' burnout, it is vital to use multilevel, evidence-based approaches. These interventions may include increasing awareness of the risks and preparing for potential occupational stress and burnout; promoting mindfulness and self-care practices to enhance mental well-being; enhancing organizational policies and procedures to address burnout among healthcare workers; and ensuring the availability of optimal mental health services, including the use of digital technologies to address workplace stress and facilitate mental health interventions. Conclusion: The interventions to prevent and reduce HCW burnout during the COVID-19 pandemic include supporting HCWs individually, improving work environments, and addressing health system factors contributing to burnout complemented by interventions aimed at enhancing work culture.

Indexed as

burnoutCOVID-19 pandemichealthcare workersprevention measurespsychological consequences

Identifiers

PMID39185505
PMCPMC11342329

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.