Evidence map›Paper›PMID 39379913›Full record

ArticleBMC nursing2024

Exploring the protective capabilities developed by hospital nurses to deflect workplace violence: a qualitative study in Taiwan.

Tsu-Chi Wang, Ting-Ya Kuo, Tao-Hsin Tung, Peter Y Chen, Fu-Li Chen

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Observational
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.

Tsu-Chi WangNew Taipei City Hospital, No. 3, Sec. 1, New Taipei Blvd., Sanchong Dist, New Taipei City, 241204, Taiwan.
Ting-Ya KuoDepartment of Public Health, Fu Jen Catholic University, No.510, Zhongzheng Rd., Xinzhuang Dist, New Taipei City, 242062, Taiwan.
Tao-Hsin TungEvidence-based Medicine Center, Taizhou Hospital of Zhejiang province affiliated to Wenzhou Medical University, No. 150, Ximen Road of Linhai City, Zhejiang, Taizhou, 317000, China.
Peter Y ChenDepartment of Psychological Sciences, Auburn University, Auburn, AL, 36849, USA.
Fu-Li ChenDepartment of Public Health, Fu Jen Catholic University, No.510, Zhongzheng Rd., Xinzhuang Dist, New Taipei City, 242062, Taiwan. 026644@mail.fju.edu.tw.ORCID https://orcid.org/0000-0002-3869-1631

Funding

Taiwan Ministry of Science and Technology MOST104-2511-S-030-002-MY2
6 · The paper itself

Abstract

backgroundWorkplace violence is a serious safety hazard in the healthcare sector and has attracted much attention worldwide, especially for nursing staff. Equipping nursing personnel with protective capabilities for workplace violence can reduce the risk they face in work settings. This study explored scenarios of common violent patient-nurse conflicts in the workplace of hospital nursing staff and their capabilities for de-escalating such conflicts.

methodsWe used a qualitative content analysis for this study. Qualitative interviews were conducted with 21 nurses in two teaching hospitals in New Taipei City, Taiwan, until data saturation was reached. The data were transcribed, encoded, and analyzed and similar concepts were grouped under the same category.

resultsFour categories of workplace violence scenarios common to hospital nursing staff were identified: unreasonable requests, caring for high-risk patients, long waiting times for medical consultation, and close contact when caring for patients. Two protective capabilities were recognized: communication and interpersonal capabilities and problem-solving skills. Each scenario may require a different combination of abilities.

conclusionOur findings suggest that a violence-prevention training program could be designed for various workplace violence scenarios to enhance nurses' abilities to de-escalate workplace violence in hospitals.

Indexed as

CapabilitiesNursesSituationTaiwanWorkplace violence

Identifiers

PMID39379913
PMCPMC11463082

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LicenceCC BY-NC-ND
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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.