Evidence map›Paper›PMID 40225669›Full record

ArticleJournal of nursing management2023

Predicting Critical Care Nurses' Intention to Use Physical Restraints in Intubated Patients: A Structural Equation Model.

Yajun Ma, Nianqi Cui, Xiaoli Li, Jiayi Li, Yuping Zhang, Yonggang Liu, Jingfen Jin

Abstract read
In one paragraph

Article in Journal of nursing management, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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.

Yajun MaNursing Department, The Second Affiliated Hospital of Zhejiang University School of Medicine (SAHZU), Hangzhou, China.ORCID 0000-0002-1552-7732
Nianqi CuiSchool of Nursing, Kunming Medical University, Kunming, China.ORCID 0000-0002-7963-4887
Xiaoli LiFirst Clinic, Kunming Medical University School and Hospital of Stomatology, Kunming, China.ORCID 0009-0002-4006-401X
Jiayi LiDepartment of Critical Care Medicine, Yan'An Hospital of Kunming City, Kunming, China.ORCID 0009-0004-2665-2709
Yuping ZhangNursing Department, The Second Affiliated Hospital of Zhejiang University School of Medicine (SAHZU), Hangzhou, China.ORCID 0000-0002-4374-8831
Yonggang LiuDepartment of Critical Care Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, China.ORCID 0009-0003-2097-6588
Jingfen JinNursing Department, The Second Affiliated Hospital of Zhejiang University School of Medicine (SAHZU), Hangzhou, China.ORCID 0000-0001-7671-3590

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: To identify the factors influencing critical care nurses' intention of physical restraint in intubated patients. Background: Physical restraint reduction has been advocated by many international institutions, nurses are the main physical restraint decision-makers, and it is critical to identify the factors influencing physical restraint intention from nurses' perspective. Methods: A cross-sectional study was conducted among critical care nurses in China from February 2022 to March 2022. Results: The model showed a good model fit ( Conclusions: The study revealed that ethical conflict, attitude, subjective norm, and perceived behavioral control were positive predictors of physical restraint intention among intubated patients from nurses' perspectives.

Indexed as

Critical Care NursingIntentionNursesRestraint, PhysicalAdultAttitude of Health PersonnelChinaCross-Sectional StudiesFemaleHumansIntensive Care UnitsLatent Class AnalysisMaleMiddle AgedSurveys and Questionnaires

Identifiers

PMID40225669
PMCPMC11919005

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.