Evidence map›Paper›PMID 34983516›Full record

ArticleBMC nursing2022

Registered nurses: can our supply meet the demand during a disaster?

Yin Li, Jason M Hockenberry, Jiaoan Chen, Jeannie P Cimiotti

Erratum issuedAbstract 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. An erratum has been issued. 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. Article
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Yin LiNell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Road NE, Atlanta, Georgia, 30322-4027, USA. yin.li@emory.edu.ORCID http://orcid.org/0000-0002-3852-8623
Jason M HockenberryDepartment of Health Policy and Management, School of Public Health, Yale University, 60 College Street, New Haven, CT, 06520-0834, USA.
Jiaoan ChenShenzhen BGI Co, Ltd, Shenzhen, China.
Jeannie P CimiottiNell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Road NE, Atlanta, Georgia, 30322-4027, USA.

Funding

National Council of State Boards of Nursing R101019
6 · The paper itself

Abstract

backgroundDeath and destructions are often reported during natural disasters; yet little is known about how hospitals operate during disasters and if there are sufficient resources available for hospitals to provide ongoing care during these catastrophic events. The purpose of this study was to determine if the State of New Jersey had a supply of registered nurses (RNs) that was sufficient to meet the needs of hospitalized patients during a natural disaster - Hurricane Sandy.

methodsSecondary data were used to forecast the demand and supply of New Jersey RNs during Hurricane Sandy. Data sources from November 2011 and 2012 included the State Inpatient Databases (SID), American Hospital Association (AHA) Annual Survey on hospital characteristics and staffing data from New Jersey Department of Health. Three models were used to estimate the RN shortage for each hospital, which was the difference between the demand and supply of RN full-time equivalents.

resultsData were available on 66 New Jersey hospitals, more than half of which experienced a shortage of RNs during Hurricane Sandy. For hospitals with a RN shortage in ICUs, a 20% increase in observed RN supply was needed to meet the demand; and a 10% increase in observed RN supply was necessary to meet the demand for hospitals with a RN shortage in non-ICUs.

conclusionFindings from this study suggest that many hospitals in New Jersey had a shortage of RNs during Hurricane Sandy. Efforts are needed to improve the availability of nurse resources during a natural disaster.

Indexed as

DemandNatural disasterRegistered nursesSupply

Identifiers

PMID34983516
PMCPMC8724595

What OpenQuestion holds

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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.