Evidence map›Paper›PMID 39232772›Full record

ArticleBMC nursing2024

Evaluation of the effectiveness of a segmented alternating shift pattern based on wearable vital signs monitoring devices during COVID-19: a cross-sectional study.

Meilian Xie, Zhiyun Zhang, Ronghua Jin, Xiaoyou Chen, Zhe Liu, Jianping Ma, Wenying Qiao

Abstract read
In one paragraph

Article in BMC nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Meilian XieNursing Management Department, Beijing Ditan Hospital Capital Medical University, Beijing, China.
Zhiyun ZhangNursing Management Department, Beijing Ditan Hospital Capital Medical University, Beijing, China. zzy1968chn@mail.ccmu.edu.cn.
Ronghua JinNational Center for Infectious Disease, Beijing Ditan Hospital Capital Medical University, Beijing, China.
Xiaoyou ChenNational Center for Infectious Disease, Beijing Ditan Hospital Capital Medical University, Beijing, China.
Zhe LiuOrthopedic and Urologic Surgery Department, Beijing Ditan Hospital Capital Medical University, Beijing, China.
Jianping MaGeneral Surgery Department, Beijing Ditan Hospital Capital Medical University, Beijing, China.
Wenying QiaoNational Center for Infectious Disease, Beijing Ditan Hospital Capital Medical University, Beijing, China.

Funding

Capital's Funds for Health Improvement and Research (CFH) 2022-1G-2173
6 · The paper itself

Abstract

aimTo explore the feasibility and effectiveness of an alternating shift pattern in two-hour segments in the state of emergency management of public health events.

methodsA cross-sectional research design was conducted in our study. The nursing staff working in the admission ward of suspected or confirmed patients with COVID-19 in December 2022 were selected as subjects by the whole-group sampling method. We adopted a shift pattern of alternating shifts every two-hour in the isolation wards. The vital signs parameters were captured by uniformly wearing the multi-signs sensing devices, and the questionnaires collected the symptoms.

resultsSixty-seven female participants, with a mean age of 33.09 ± 5.96 years, were included in this study. Measurements of body temperature (T), blood oxygen saturation (SpO

conclusionsThe alternating shift pattern in two-hour segments can effectively maintain the physical well-being of nurses wearing PPE without increasing the burden on nursing staff or the wastage of protective materials. This approach is recommended for addressing similar public health events in the future.

Indexed as

COVID-19Human resourcesNursingShift pattern

Identifiers

PMID39232772
PMCPMC11375973

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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.