Evidence map›Paper›PMID 34285785›Full record

ArticleJournal of healthcare engineering2021

The Current Situation and Influencing Factors of the Alarm Fatigue of Nurses' Medical Equipment in the Intensive Care Unit Based on Intelligent Medical Care.

Yunxia Zhao, Minlin Wan, Huisong Liu, Mei Ma

RetractedAbstract readRetracted Publication
In one paragraph

Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Yunxia ZhaoIntensive Care Unit, Qingdao Municipal Hospital, Qingdao 266011, Shandong, China.
Minlin WanIntensive Care Unit, Qingdao Municipal Hospital, Qingdao 266011, Shandong, China.
Huisong LiuNursing Department, Qingdao Municipal Hospital, Qingdao 266011, Shandong, China.
Mei MaIntensive Care Unit, Qingdao Municipal Hospital, Qingdao 266011, Shandong, China.ORCID 0000-0001-5329-6587

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

After rapid development and reform, the health level and medical diagnosis and treatment capabilities of Chinese residents have been significantly improved, and high-quality medical resources have significantly improved the life safety and health of the masses. As the most concentrated area of medical equipment in the hospital, the intensive care unit produces the most alarms during the operation of the equipment. The intensive care unit nurses are responsible for heavier nursing work, and the problem of alarming in other departments is more prominent. Therefore, this paper presents an analysis and research on the current situation and influencing factors of the alarm fatigue of nurse medical equipment in the intensive care unit based on intelligent medicine. This article uses a variety of related methods such as literature data method and questionnaire survey method to deeply study the theoretical knowledge of intelligent medical treatment, medical equipment alarm fatigue device, and so on. The logistic regression analysis method is introduced to classify its influencing factors, and the analysis experiment on the influencing factors of the medical equipment alarm fatigue of nurses in the intensive care unit is designed. The nurses' cognition of clinical alarms and the analysis of clinical alarm fatigue questionnaire data are studied. The alarm fatigue of nurses in the intensive care unit is at a severe level, which needs to be taken seriously in the intensive care unit. Unmarried, high-level positions, long working years, high professional titles, and high education are negatively correlated with alarm fatigue (

Indexed as

Clinical AlarmsNursesHumansIntensive Care UnitsMonitoring, PhysiologicSurveys and Questionnaires

Identifiers

PMID34285785
PMCPMC8275382

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.