Evidence map›Paper›PMID 34728676›Full record

ArticleScientific reports2021

Effects of registered nurse staffing levels, work environment, and education levels on adverse events in nursing homes.

Seonhwa Choi, Eunhee Cho, Eunkyo Kim, Kyongeun Lee, Soo Jung Chang

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

5 authors.

Seonhwa ChoiCollege of Nursing, Yonsei University, 50-1, Yonsei-ro, Seodaemoon-Gu, Seoul, 03722, South Korea.
Eunhee ChoMo-Im Kim Nursing Research Institute, Yonsei University College of Nursing, 50-1, Yonsei-ro, Seodaemoon-Gu, Seoul, 03722, South Korea.
Eunkyo KimCollege of Nursing, Yonsei University, 50-1, Yonsei-ro, Seodaemoon-Gu, Seoul, 03722, South Korea.
Kyongeun LeeDepartment of Nursing, Tongmyong University, 428, Sinseon-ro, Nam-gu, Busan, 48520, South Korea.
Soo Jung ChangDepartment of Nursing, Gangneung-Wonju National University, 150, Namwon-ro, Heungeop-myeon, Wonju, 26403, South Korea. sjchang@gwnu.ac.kr.

Funding

National Research Foundation grant funded by the government of the Republic of Korea NRF-2014R1A1A3049936
6 · The paper itself

Abstract

This study examined the effects of nurse staffing levels, work environment, and education levels on adverse events in nursing homes. A cross-sectional study was conducted involving 216 nurses working in 62 nursing homes in South Korea, using self-reported questionnaires and data from the National Health Insurance Service of South Korea. A logistic regression model was used to investigate the effects of nurse staffing levels, work environment, and nursing education levels on the adverse events experienced by residents. An increase of one resident per nurse was significantly associated with a higher incidence of medication error, pressure ulcers and urinary tract infections. A poor work environment increased the incidence of adverse events. Compared to nurses with a bachelor's degree or higher, those with diplomas reported increased incidence rates of pressure ulcers. Improving the health outcomes of residents in nursing homes requires efforts that strengthen the nursing workforce in terms of numbers and educational level, and which improve their work environment at institutional and policy levels.

Indexed as

Educational StatusWorkplaceAdultAgedCross-Sectional StudiesFemaleHumansMaleMedication ErrorsMiddle AgedNursing HomesNursing StaffPressure UlcerQuality of Health CareSurveys and QuestionnairesUrinary Tract Infections

Identifiers

PMID34728676
PMCPMC8563772

What OpenQuestion holds

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LicenceCC BY
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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.