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Workplace Predictors of Quality and Safe Patient Care Delivery Among Nurses Using Machine Learning Techniques
Farinaz Havaei1, Xuejun Ryan Ji, Sheila A Boamah
1University of British Columbia (UBC) School of Nursing, Vancouver, British Columbia, Canada (Dr Havaei); UBC Department of Educational and Counselling Psychology and Special Education, Vancouver, British Columbia, Canada (Dr Ji); and McMaster University School of Nursing, Hamilton, Ontario, Canada (Dr Boamah).
Background:
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.
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