A model for predicting student nurse attrition during pre-registration training: A retrospective observations study...

Area of Science:

• Nursing Education
• Higher Education Analytics
• Student Retention

Background:

• Global shortage of trained nurses impacts patient safety.
• Aging nursing workforce exacerbates care delivery pressures.
• High undergraduate nursing attrition rates (approx. 24%) hinder workforce stabilization.

Purpose of the Study:

• Identify patterns predicting nursing student attrition risk.
• Develop a predictive model for individual attrition risk.
• Inform targeted student retention strategies.

Main Methods:

• Employed a Knowledge Discovery in Databases (KDD) methodology.
• Conducted an observational analysis of routinely collected student data (n=1840).
• Utilized university business intelligence systems for data modeling and step-wise binomial regression analysis.

Main Results:

• Identified key predictors of nursing student attrition.
• Students returning from intermittent study periods showed increased attrition likelihood.
• Male and non-mature students demonstrated a higher propensity to leave the course.

Conclusions:

• First study to link study intermittence to nursing student attrition.
• Leveraged pre-existing university business intelligence systems for predictive modeling.
• Highlights potential for tailored retention strategies beyond demographic assessment.