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Workplace Predictors of Quality and Safe Patient Care Delivery Among Nurses Using Machine Learning Techniques
Working in unhealthy environments is associated with negative nurse and patient outcomes. Previous body of evidence in this area is limited as it investigated only a few factors within nurses' workplaces. PURPOSE: The purpose of this study was to identify the most important workplace factors pr...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer Health, Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860211/ https://www.ncbi.nlm.nih.gov/pubmed/34593739 http://dx.doi.org/10.1097/NCQ.0000000000000600 |
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author | Havaei, Farinaz Ji, Xuejun Ryan Boamah, Sheila A. |
author_facet | Havaei, Farinaz Ji, Xuejun Ryan Boamah, Sheila A. |
author_sort | Havaei, Farinaz |
collection | PubMed |
description | Working in unhealthy environments is associated with negative nurse and patient outcomes. Previous body of evidence in this area is limited as it investigated only a few factors within nurses' workplaces. PURPOSE: The purpose of this study was to identify the most important workplace factors predicting nurses' provision of quality and safe patient care using a 13-factor measure of workplace conditions. METHODS: A cross-sectional correlational survey study involving 4029 direct care nurses in British Columbia was conducted using random forest data analytics methods. RESULTS: Nurses' reports of healthier workplaces, particularly workload management, psychological protection, physical safety and engagement, were associated with higher ratings of quality and safe patient care. CONCLUSION: These workplace conditions are perceived to impact patient care through influencing nurses' mental health. To ensure a high standard of patient care, data-driven policies and interventions promoting overall nurse mental health and well-being are urgently required. |
format | Online Article Text |
id | pubmed-8860211 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Wolters Kluwer Health, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88602112022-02-24 Workplace Predictors of Quality and Safe Patient Care Delivery Among Nurses Using Machine Learning Techniques Havaei, Farinaz Ji, Xuejun Ryan Boamah, Sheila A. J Nurs Care Qual Articles Working in unhealthy environments is associated with negative nurse and patient outcomes. Previous body of evidence in this area is limited as it investigated only a few factors within nurses' workplaces. PURPOSE: The purpose of this study was to identify the most important workplace factors predicting nurses' provision of quality and safe patient care using a 13-factor measure of workplace conditions. METHODS: A cross-sectional correlational survey study involving 4029 direct care nurses in British Columbia was conducted using random forest data analytics methods. RESULTS: Nurses' reports of healthier workplaces, particularly workload management, psychological protection, physical safety and engagement, were associated with higher ratings of quality and safe patient care. CONCLUSION: These workplace conditions are perceived to impact patient care through influencing nurses' mental health. To ensure a high standard of patient care, data-driven policies and interventions promoting overall nurse mental health and well-being are urgently required. Wolters Kluwer Health, Inc. 2022-04 2021-09-28 /pmc/articles/PMC8860211/ /pubmed/34593739 http://dx.doi.org/10.1097/NCQ.0000000000000600 Text en © 2021 The Authors. Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Articles Havaei, Farinaz Ji, Xuejun Ryan Boamah, Sheila A. Workplace Predictors of Quality and Safe Patient Care Delivery Among Nurses Using Machine Learning Techniques |
title | Workplace Predictors of Quality and Safe Patient Care Delivery Among Nurses Using Machine Learning Techniques |
title_full | Workplace Predictors of Quality and Safe Patient Care Delivery Among Nurses Using Machine Learning Techniques |
title_fullStr | Workplace Predictors of Quality and Safe Patient Care Delivery Among Nurses Using Machine Learning Techniques |
title_full_unstemmed | Workplace Predictors of Quality and Safe Patient Care Delivery Among Nurses Using Machine Learning Techniques |
title_short | Workplace Predictors of Quality and Safe Patient Care Delivery Among Nurses Using Machine Learning Techniques |
title_sort | workplace predictors of quality and safe patient care delivery among nurses using machine learning techniques |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860211/ https://www.ncbi.nlm.nih.gov/pubmed/34593739 http://dx.doi.org/10.1097/NCQ.0000000000000600 |
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