Predicting late dropout from nursing education or early dropout from the profession.

Abstract

AimTo identify predictors of late academic or early career dropout, and derive a simple model for identifying nursing students and novice nurses with significant increased dropout risk. BackgroundDropout from nursing school and the nursing profession is of great concern for students, educators, as well as graduated nurses. Nurse shortages are a major problem in healthcare worldwide (Drennan & Ross, 2019). Retention of nursing students and novice nurses can contribute to reducing the deficits (Smith-Wacholz et al., 2019). Little is known about the predictors of dropout among nursing students in the later years of their degree programme (late dropout) and early nurse dropout from the profession. DesignProspective cohort study with three years of follow-up, among 406 third-year nursing students of the Bachelor of Nursing programme of Rotterdam University of Applied Sciences. MethodsData were collected between May 2016 and February 2019 using a self-administered questionnaire. Backward binary multiple logistic regression analyses were used to build a prediction model for dropout. ResultsDropout from nursing education and at the start of the nursing career totalled 12%. Twelve factors, including male sex (OR 3.76, 95% CI 1.41–10.04), age (OR 1.06, 95% CI 1.00–1.12), migration background (OR 2.42, 95% CI 1.10–5.32), clinical placement setting (including mental healthcare; OR 0.18, 95% CI 0.04–0.83), musculoskeletal symptoms (OR 1.20, 95% CI 1.02–1.42) and psychosocial work characteristics (including exposure to violence; OR 3.13, 95% CI 1.25–7.81) were statistically significant predictors in our dropout model. The explained variance of the final model was 26%. ConclusionThe study highlights the importance of taking musculoskeletal and mental health symptoms, psychosocial work characteristics, as well as sex, age and migration background into consideration as predictors for dropout among nursing students and novice nurses. This study is a first step towards a predictive model that helps identifying high-risk groups.