Evidence map›Paper›PMID 29929520›Full record

ArticleBMC health services research2018

What is behind high turnover: a questionnaire survey of hospital nursing care workers in Shanghai, China.

Yan Wang, Huiyun Yuan

Abstract read
In one paragraph

Article in BMC health services research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

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

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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, 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

2 authors.

Yan WangRenji Hospital Shanghai Jiao Tong University School of Medicine, NO.160 Pujian Road, ShangHai, 200127, China.
Huiyun YuanRenji Hospital Shanghai Jiao Tong University School of Medicine, NO.160 Pujian Road, ShangHai, 200127, China. 1176181116@qq.com.ORCID 0000-0001-5419-8268

Funding

The Shanghai Municipal People's Government Decision Consulting Research Projects (2015-D-25). 2015-D-25
6 · The paper itself

Abstract

backgroundCurrently, hospital nursing care workers (hereafter referred to as HNCWs) have become an important part of the healthcare system in China. They exist in nearly all of the public hospitals and in some private hospitals, making up 20 to 30% of the total nursing staff and providing 30 to 40% of basic nursing care for patients. However, many studies have shown that the turnover rate of HNCWs is very high, with average annual rates of 20 to 45%. We conducted this survey to explore their turnover intentions and related factors and present some suggestions to improve their retention rate.

methodsA total of 514 HNCWs employed at 11 hospitals in Shanghai participated in this study. The inclusion criteria were as follows: (1) being a certified HNCW, (2) having worked as an HNCW for more than 1 year, and (3) volunteering to take part in the survey.

resultsThe overall turnover intention of the HNCWs was 41.3%. Influencing factors include education (βeta = 0.201, P = 0.000), wages (βeta = - 0.920, P = 0.000), management satisfaction, (βeta = - 0.213, P = 0.000), satisfaction with wages (βeta = - 0.612, P = 0.000), satisfaction with working hours (βeta = - 0.270, P = 0.000), satisfaction with their own work (βeta = - 0.066, P = 0.027), work stress (βeta = 0.726, P = 0.000), enjoyment of the job (βeta = - 0.141, P = 0.000) and hours of sleep (βeta = - 0.046, P = 0.037).

conclusionsDecreasing HNCWs' turnover intentions and the overall turnover rate is important for improving the quality of healthcare. More attention should be paid to this issue in the enactment of health policy.

Indexed as

AdultBurnout, ProfessionalChinaCross-Sectional StudiesFemaleHospitals, GeneralHumansJob SatisfactionMaleMiddle AgedNursing Staff, HospitalPersonnel LoyaltyPersonnel TurnoverSalaries and Fringe BenefitsWorkplaceHospital nursing care workerRelated factorsTurnover intention

Identifiers

PMID29929520
PMCPMC6013857

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.