Evidence map›Paper›PMID 35118507›Full record

ArticleSouthern medical journal2022

Disaster Preparedness Training Needs of Healthcare Workers at the US Department of Veterans Affairs.

Michelle D Balut, Claudia Der-Martirosian, Aram Dobalian

Abstract read
In one paragraph

Article in Southern medical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
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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

3 authors.

Michelle D BalutFrom the Veterans Emergency Management Evaluation Center, US Department of Veterans Affairs, North Hills, California.ORCID 0038-4348/0-2000/115-158
Claudia Der-MartirosianFrom the Veterans Emergency Management Evaluation Center, US Department of Veterans Affairs, North Hills, California.
Aram DobalianFrom the Veterans Emergency Management Evaluation Center, US Department of Veterans Affairs, North Hills, California.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTraining healthcare workers in disaster preparedness has been shown to increase their willingness and ability to report to work during disasters. Little is known, however, about the relation between sociodemographic, household, and workforce characteristics and the desire for such training. Accordingly, this study aimed to assess healthcare workers' desire for additional workforce preparedness training, and the determinants that influence the need for such training, for three types of disasters (natural, pandemic, manmade).

methodsThe US Department of Veterans Affairs (VA) Preparedness Survey was a random, anonymous, Web-based questionnaire fielded nationwide (October-December 2018). Multivariate, logistic regression analyses were conducted.

resultsIn total, 4026 VA employees, clinical and nonclinical, responded. A total of 61% of respondents wanted additional training for natural, 63% for pandemic, and 68% for manmade disasters. VA supervisors (natural: odds ratio [OR] 1.28, pandemic: OR 1.33, manmade: OR 1.25,

conclusionsIdentifying which factors encourage participation in disaster preparedness training can help hospitals and other healthcare providers create targeted training and educational materials to better prepare all hospital staff for future disasters.

Indexed as

AdultAttitude of Health PersonnelCivil DefenseFemaleHealth PersonnelHealth Services Needs and DemandHumansLogistic ModelsMaleMiddle AgedNeeds AssessmentOdds RatioSurveys and QuestionnairesUnited StatesUnited States Department of Veterans Affairs

Identifiers

PMID35118507
PMCPMC8785770

What OpenQuestion holds

Textmetadata
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Registered trials

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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.