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A Mixed Methods Analysis of Recognition Satisfaction and Preferences Among Nurses
Dana M Morson1, Joseph R Travis, Aoyjai P Montgomery
1Author Affiliation: Nursing Professional Development Specialist (Dr Morson, Dr Travis, Carter), Nurse Wellness Manager (Beam), Associate Professor (Dr Layton), Professor (Dr Polancich), and Professor and Rachel Z. Booth Endowed Chair (Dr Patrician), School of Nursing, and Scientist II (Dr Montgomery), Department of Epidemiology, School of Public Health, University of Alabama at Birmingham.
Objective:
This study assesses the effects of recognition satisfaction on key outcomes and preferences among nursing staff.
Background:
Meaningful recognition is vital in nursing, impacting burnout, intent to leave, and moral distress.
Methods:
A mixed methods cross-sectional study was conducted with nurse leaders, staff nurses, nursing professionals, and support staff, including quantitative questions on demographics, well-being, organizational support, resilience, and recognition satisfaction, along with qualitative questions on recognition preferences.
Results:
Nurses' recognition satisfaction was significantly and inversely related to burnout, intent to leave, and moral distress in our sample. Key themes in order of decreasing importance were financial incentives, acknowledgment, fairness, positive feedback, and listening leadership. Those preferring financial incentives reported the lowest perceived organizational support and the lowest well-being of all 5 groups. Participants valuing listening leadership experienced the highest burnout and intent to leave of all groups. Those preferring positive feedback had the highest moral distress.
Conclusions:
Tailoring recognition programs to preferences of a workforce unit may enhance well-being, perceived organizational support, and retention in nursing including nursing support staff.
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The Scientific Method in Nursing Process
When using research findings to change practice, one must understand the process used to guide a study. The scientific method is a systematic, step-by-step process that supports the data's validity, reliability, and generalizability. As a result, findings can be...

