Other Titles
Workplace Environmental Predictors of Intent to Leave Current Nursing Job in Newly Licensed RNs [Title Slide]
Abstract
Background: High rates of registered nurse (RN) turnover in hospitals strain their ability to keep fully staffed. Poor nurse staffing leads to poor patient, nurse, and organizational outcomes (Rae et al., 2021; Shin et al., 2018, 2019). High rates of RN turnover are also costly for healthcare organizations (NSI Nursing Solutions, 2023). While RN turnover rates in US hospitals have increased significantly since the COVID-19 pandemic (22.5% in 2022 compared with 15.9% in 2019), historically, newly licensed RNs have even higher rates of turnover than their more experienced counterparts (Kovner et al., 2016). It is critically important to understand the predicters of turnover in this population to increase retention.
Aims: To identify individual and workplace predictors of turnover intention in newly licensed RNs through a secondary data analysis of the “Newly Licensed Registered Nurse Cohort 3” survey data from 2016 (Kovner & Brewer, 2020).
Methods: Participants who obtained their first nursing license between August 1, 2014 and July 31, 2015 completed the Newly Licensed RN Survey to describe newly licensed RNs work characteristics and patterns (n=1110). A secondary analysis was conducted to identify characteristics that predict participant’s intent to leave their current nursing job. Latent class analysis identified classes of nursing job difficulty, and included items asking about job difficulty due to workplace environmental factors including unit supervisor, inadequate help, lack of supply, constant interruption, and incorrect instruction. Chi-square analyses were run to explore latent class differences in other work-related factors. Logistic regression analysis explored the association between job difficulty class and intent to leave current nursing job, controlling for other relevant individual and workplace factors.
Results: A 4-class solution was identified, with Class 1 (n=340) representing low levels of job difficulty overall, Class 2 (n=343) representing a chaotic work environment with supportive leadership, Class 3 (n=248) representing an overworked group but with organizational/leadership support, and Class 4 (n=179) representing high levels of job difficulty overall. Chi-square analyses showed associations between job difficulty class and other organizational/job-related factors, including patient safety, relationship with physicians, and verbal abuse from patients, physicians, and other staff. Multiple logistic regression analysis showed that membership in job difficulty class 3 (OR 2.06, p=0.007) and class 4 (OR 3.93, p< 0.0001) predicted higher odds of intent to leave compared with class 1 (low difficulty). Other workplace factors associated with decreased odds of intent to leave include good relationships with physicians (OR 0.47, p=0.004) and working in a hospital setting (OR 0.47, p=0.001). Reporting patient safety issues on the unit was associated with increased odds of intent to leave (OR 2.06, p< 0.0001). Individual factors associated with intent to leave include working preferred shift and schedule, and availability of other nursing jobs.
Discussion: Job difficulty factors reported by newly licensed nurses are important in predicting intent to leave current nursing job. Class 3 shows that despite organizational and supervisor support, inadequate staffing (that results in overworked nurses) causes nurses to seriously consider leaving their nursing job. Healthcare organizations must improve staffing and workflow to improve nurse retention.
Notes
Presenter notes available in attached slide deck.
References:
Kovner, C. T., & Brewer, C. (2020). Newly Licensed Registered Nurse New Cohort 3 Survey, 2016 (Version 2) [dataset]. Inter-university Consortium for Political and Social Research. https://doi.org/10.3886/ICPSR36821
Kovner, C. T., Djukic, M., Fatehi, F. K., Fletcher, J., Jun, J., Brewer, C., & Chacko, T. (2016). Estimating and preventing hospital internal turnover of newly licensed nurses: A panel survey. International Journal of Nursing Studies, 60, 251–262. https://doi.org/10.1016/j.ijnurstu.2016.05.003
NSI Nursing Solutions. (2023). 2023 NSI National Health Care Retention & RN Staffing Report. NSI Nursing Solutions, Inc.
Rae, P. J. L., Pearce, S., Greaves, P. J., Dall’Ora, C., Griffiths, P., & Endacott, R. (2021). Outcomes sensitive to critical care nurse staffing levels: A systematic review. Intensive and Critical Care Nursing, 67, 103110. https://doi.org/10.1016/j.iccn.2021.103110
Shin, S., Park, J.-H., & Bae, S.-H. (2018). Nurse staffing and nurse outcomes: A systematic review and meta-analysis. Nursing Outlook, 66(3), 273–282. https://doi.org/10.1016/j.outlook.2017.12.002
Shin, S., Park, J.-H., & Bae, S.-H. (2019). Nurse staffing and hospital-acquired conditions: A systematic review. Journal of Clinical Nursing, 28(23–24), 4264–4275. https://doi.org/10.1111/jocn.15046
Sigma Membership
Non-member
Type
Presentation
Format Type
Text-based Document
Study Design/Type
Cross-Sectional
Research Approach
Quantitative Research
Keywords:
Personnel Turnover, Intent to Leave, Personnel Retention, Nurse Attitudes, Registered Nurses, Newly Licensed RNs, New Graduate Nurses
Recommended Citation
Leep-Lazar, Kathryn Joyce, "Individual and Workplace Environmental Predictors of Intent to Leave Current Nursing Job" (2026). Creating Healthy Work Environments (CHWE). 78.
https://www.sigmarepository.org/chwe/2024/presentations_2024/78
Conference Name
Creating Healthy Work Environments
Conference Host
Sigma Theta Tau International
Conference Location
Washington, DC, USA
Conference Year
2024
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All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record. All permission requests should be directed accordingly and not to the Sigma Repository. All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Date of Issue
2026-02-23
Individual and Workplace Environmental Predictors of Intent to Leave Current Nursing Job
Washington, DC, USA
Background: High rates of registered nurse (RN) turnover in hospitals strain their ability to keep fully staffed. Poor nurse staffing leads to poor patient, nurse, and organizational outcomes (Rae et al., 2021; Shin et al., 2018, 2019). High rates of RN turnover are also costly for healthcare organizations (NSI Nursing Solutions, 2023). While RN turnover rates in US hospitals have increased significantly since the COVID-19 pandemic (22.5% in 2022 compared with 15.9% in 2019), historically, newly licensed RNs have even higher rates of turnover than their more experienced counterparts (Kovner et al., 2016). It is critically important to understand the predicters of turnover in this population to increase retention.
Aims: To identify individual and workplace predictors of turnover intention in newly licensed RNs through a secondary data analysis of the “Newly Licensed Registered Nurse Cohort 3” survey data from 2016 (Kovner & Brewer, 2020).
Methods: Participants who obtained their first nursing license between August 1, 2014 and July 31, 2015 completed the Newly Licensed RN Survey to describe newly licensed RNs work characteristics and patterns (n=1110). A secondary analysis was conducted to identify characteristics that predict participant’s intent to leave their current nursing job. Latent class analysis identified classes of nursing job difficulty, and included items asking about job difficulty due to workplace environmental factors including unit supervisor, inadequate help, lack of supply, constant interruption, and incorrect instruction. Chi-square analyses were run to explore latent class differences in other work-related factors. Logistic regression analysis explored the association between job difficulty class and intent to leave current nursing job, controlling for other relevant individual and workplace factors.
Results: A 4-class solution was identified, with Class 1 (n=340) representing low levels of job difficulty overall, Class 2 (n=343) representing a chaotic work environment with supportive leadership, Class 3 (n=248) representing an overworked group but with organizational/leadership support, and Class 4 (n=179) representing high levels of job difficulty overall. Chi-square analyses showed associations between job difficulty class and other organizational/job-related factors, including patient safety, relationship with physicians, and verbal abuse from patients, physicians, and other staff. Multiple logistic regression analysis showed that membership in job difficulty class 3 (OR 2.06, p=0.007) and class 4 (OR 3.93, p< 0.0001) predicted higher odds of intent to leave compared with class 1 (low difficulty). Other workplace factors associated with decreased odds of intent to leave include good relationships with physicians (OR 0.47, p=0.004) and working in a hospital setting (OR 0.47, p=0.001). Reporting patient safety issues on the unit was associated with increased odds of intent to leave (OR 2.06, p< 0.0001). Individual factors associated with intent to leave include working preferred shift and schedule, and availability of other nursing jobs.
Discussion: Job difficulty factors reported by newly licensed nurses are important in predicting intent to leave current nursing job. Class 3 shows that despite organizational and supervisor support, inadequate staffing (that results in overworked nurses) causes nurses to seriously consider leaving their nursing job. Healthcare organizations must improve staffing and workflow to improve nurse retention.
Description
This is a secondary data analysis examining workplace predictors of intent to leave current nursing job in newly licensed RNs. A latent class analysis was conducted to identify classes of nursing job difficulty. Multiple logistic regression analyses were conducted to identify whether class membership was associated with intent to leave.