Evidence map›Paper›PMID 39143575›Full record

ReviewBMC nursing2024

Overcoming challenges in nursing disaster preparedness and response: an umbrella review.

Abdulellah Al Thobaity

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BMC nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 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

28 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

1 author.

Abdulellah Al ThobaityMedical Surgical Nursing, College of Nursing, Taif Unoversity, P. O. Box 11099, Taif, 21944, Saudi Arabia. abdulellah.althobaity@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Disaster nursing plays a vital role in addressing the health needs of vulnerable populations affected by large scale emergencies. However, disaster nursing faces numerous challenges, including preparedness, logistics, education, ethics, recovery and legalities. To enhance healthcare system effectiveness during crises, it is essential to overcome these issues. This umbrella review, conducted using the Joanna Briggs Institute (JBI) methodology, synthesizes data from 24 studies to identify key strategies for improving disaster nursing. The review highlights nine key themes: Education and Training, Research and Development, Policy and Organizational Support, Technological Advancements, Psychological Preparedness and Support, Assessment and Evaluation, Role-Specific Preparedness, Interprofessional Collaboration and Cultural Competence, and Ethics and Decision-Making. The review emphasizes the importance of education, technological advancements, psychological support, and interprofessional collaboration in bolstering disaster nursing preparedness and response efforts. These elements are crucial for enhancing patient outcomes during emergencies and contributing to a more resilient healthcare system. This comprehensive analysis provides valuable insights into the various aspects essential for enhancing disaster nursing. By implementing evidence-based strategies within these nine themes, the nursing profession can enhance its capacity to effectively manage and respond to the complex needs of disaster-affected populations, ultimately improving patient care and outcomes during emergencies.

Indexed as

Emergency response educationHealthcare system resilienceInterprofessional collaborationKeyword disaster nursingPreparedness strategies

Identifiers

PMID39143575
PMCPMC11323674

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.