Evidence map›Paper›PMID 39023894›Full record

ArticleJAMA network open2024

Changes in Registered Nurse Employment Plans and Workplace Assessments.

Christopher R Friese, Barbara R Medvec, Deanna J Marriott, Lara Khadr, Marissa Rurka Wade, Melissa Riba, Marita G Titler

Abstract read
In one paragraph

Article in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Christopher R FrieseCenter for Improving Patient and Population Health, University of Michigan, Ann Arbor.
Barbara R MedvecCenter for Improving Patient and Population Health, University of Michigan, Ann Arbor.
Deanna J MarriottCenter for Improving Patient and Population Health, University of Michigan, Ann Arbor.
Lara KhadrApplied Biostatistics Laboratory, School of Nursing, University of Michigan, Ann Arbor.
Marissa Rurka WadeCenter for Health and Research Transformation, Ann Arbor, Michigan.
Melissa RibaCenter for Health and Research Transformation, Ann Arbor, Michigan.
Marita G TitlerCenter for Improving Patient and Population Health, University of Michigan, Ann Arbor.

Funding

Complexity: Innovations in Promoting Health through Team ScienceT32NR016914 · NINR · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MARJORIE E MCCULLAGH · 2017 to 2026
$3.5M
Interdisciplinary Research Training Center in Cancer Care DeliveryT32CA236621 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Lauren P Wallner · 2020 to 2026
$2.8M
NCI NIH HHS T32 CA236621NINR NIH HHS T32 NR016914
6 · The paper itself

Abstract

Importance: The US registered nurse (RN) workforce is in flux, with high rates of burnout, intention to leave, and vacancies. Rapid, repeated assessments of the nursing workforce can help hospital executives and policymakers enact effective recruitment and retention strategies. Objective: To identify changes in practicing RNs' employment plans and workplace assessments between the 2022 and 2023 surveys. Design, Setting, and Participants: This survey study compared data collected from the Michigan Nurses' Study at 2 time points: February 22 to March 1, 2022, and May 17 to June 1, 2023. Practicing RNs with an active, unrestricted license in Michigan and a valid individual email address were included. Main Outcome and Measures: The primary outcome was nurses' intention to leave their current position within 1 year. In the 2023 survey, nurses who planned to leave were queried on their next career step and the primary reason for their planned departure. Workplace assessments included questions about abusive or violent workplace events, emotional exhaustion, job satisfaction, the practice environment's delivery of high-quality care, and the clinical setting's safety rating. Regression analysis was used to examine workplace assessments and personal factors associated with planned departures. Results: This study obtained data on 9150 nurses (6495 females [71.0%]) and 7059 nurses (5134 females [72.7%]) responding to the 2022 (response rate, 8.3%) and 2023 (response rate, 7.4%) surveys, respectively. In the 2023 survey, 32.0% (2259) of nurses planned to leave their position, compared with 39.1% (3576) in the 2022 survey. Of these nurses, 957 (41.8%) planned to leave their current employer but remain in nursing, with workloads as the most frequently cited reason (29.4% [672]). Compared with the 2022 cohort, nurses in the 2023 sample reported less workplace abuse or violence (4591 [50.2%] vs 3063 [43.4%]; P < .001), fewer understaffed shifts (4407 [48.2%] vs 2898 [41.0%]; P < .001), and less frequent use of mandatory overtime (1709 [18.7%] vs 824 [11.7%]; P < .001). Factors associated with increased likelihood for planned departures included workplace abuse or violence (odds ratio [OR], 1.39; 95% CI, 1.05-1.82) and higher emotional exhaustion scores (OR, 3.05; 95% CI, 2.38-3.91). Favorable practice environments (OR, 0.37; 95% CI, 0.22-0.62) and excellent clinical setting safety ratings (OR, 0.28; 95% CI, 0.14-0.56) were associated with lower likelihood of planned departure. Conclusions and Relevance: Results of this study showed that nurses reported improved workplace conditions in the 2023 vs the 2022 survey; however, planned departure rates, abusive or violent events, and unsafe conditions remained high, and understaffing remained a primary concern for most nurses. Health system leaders and policymakers should prioritize initiatives that support nurse retention and reduce potential workforce instability.

Indexed as

Job SatisfactionWorkplaceAdultBurnout, ProfessionalEmploymentFemaleHumansMaleMichiganMiddle AgedNursesNursing Staff, HospitalPersonnel TurnoverSurveys and QuestionnairesWorkplace Violence

Identifiers

PMID39023894
PMCPMC11258586

What OpenQuestion holds

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LicenceCC BY
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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.