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Volume 11
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10.3390/healthcare11071008
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Article

Workplace Predictors of Violence against Nurses Using Machine Learning Techniques: A Cross-Sectional Study Utilizing the National Standard of Psychological Workplace Health and Safety

by
Farinaz Havaei
1,*,
Nassim Adhami
1,
Xuyan Tang
2,
Sheila A. Boamah
3,
Megan Kaulius
1,
Emili Gubskaya
4 and
Kenton O’Donnell
1
1
School of Nursing, The University of British Columbia, Vancouver, BC V6T 2B5, Canada
2
Department of Educational and Counselling Psychology, and Special Education, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
3
School of Nursing, McMaster University, Hamilton, ON L8S 4K1, Canada
4
Faculty of Medicine, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
*
Author to whom correspondence should be addressed.
Healthcare 2023, 11(7), 1008; https://doi.org/10.3390/healthcare11071008
Submission received: 8 February 2023 / Revised: 16 March 2023 / Accepted: 27 March 2023 / Published: 1 April 2023
(This article belongs to the Section Nursing)
Review Reports Versions Notes

Abstract

Background: Nurses experience an alarming rate of violence in the workplace. While previous work has indicated that working conditions play an important role in workplace violence outcomes, these studies have not used comprehensive and systematically operationalized variables. Methods: Through cross-sectional survey responses from 4066 British Columbian nurses, we identified which of the 13 psychosocial factors, as outlined in the National Standard of Psychological Workplace Health and Safety, are most predictive of workplace violence perpetrated against nurses by patients and their visitors (Type II violence) and organizational employees (Type III violence). Results: Eighty-seven percent of respondents indicated that they had experienced Type II violence, whereas 48% indicated they had experienced Type III violence over the last year. Lack of physical safety, workload management, and psychological protection were the top three psychosocial factors in the workplace predictive of Type II violence, whereas lack of civility and respect, organizational culture, and psychological support were the top three factors associated with Type III violence. Conclusions: The findings in this study shed light on the distinct psychosocial factors in the workplace in need of investment and intervention to address Type II and III violence.
Keywords: nursing; workplace violence; machine learning nursing; workplace violence; machine learning

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MDPI and ACS Style

Havaei, F.; Adhami, N.; Tang, X.; Boamah, S.A.; Kaulius, M.; Gubskaya, E.; O’Donnell, K. Workplace Predictors of Violence against Nurses Using Machine Learning Techniques: A Cross-Sectional Study Utilizing the National Standard of Psychological Workplace Health and Safety. Healthcare 2023, 11, 1008. https://doi.org/10.3390/healthcare11071008

AMA Style

Havaei F, Adhami N, Tang X, Boamah SA, Kaulius M, Gubskaya E, O’Donnell K. Workplace Predictors of Violence against Nurses Using Machine Learning Techniques: A Cross-Sectional Study Utilizing the National Standard of Psychological Workplace Health and Safety. Healthcare. 2023; 11(7):1008. https://doi.org/10.3390/healthcare11071008

Chicago/Turabian Style

Havaei, Farinaz, Nassim Adhami, Xuyan Tang, Sheila A. Boamah, Megan Kaulius, Emili Gubskaya, and Kenton O’Donnell. 2023. "Workplace Predictors of Violence against Nurses Using Machine Learning Techniques: A Cross-Sectional Study Utilizing the National Standard of Psychological Workplace Health and Safety" Healthcare 11, no. 7: 1008. https://doi.org/10.3390/healthcare11071008

APA Style

Havaei, F., Adhami, N., Tang, X., Boamah, S. A., Kaulius, M., Gubskaya, E., & O’Donnell, K. (2023). Workplace Predictors of Violence against Nurses Using Machine Learning Techniques: A Cross-Sectional Study Utilizing the National Standard of Psychological Workplace Health and Safety. Healthcare, 11(7), 1008. https://doi.org/10.3390/healthcare11071008

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