Early identification of at-risk nursing students: a student support model

J Nurs Educ. 2008 Jun;47(6):254-9. doi: 10.3928/01484834-20080601-05.


Due to the shortage of nurses in the health care industry, colleges offering associate-degree nursing programs are beginning to pay more attention to attrition and the factors contributing to success. Alogistic regression model was used to explain the cognitive and noncognitive variables that contribute to success in a nursing fundamentals course. Although much work is necessary to fully understand first-semester nursing students' retention and success, an early identification model is explored to better support students as they enter associate-degree nursing programs.

MeSH terms

  • Adolescent
  • Adult
  • Analysis of Variance
  • Cognition
  • Education, Nursing, Associate / organization & administration*
  • Educational Measurement
  • Female
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Models, Educational
  • Models, Nursing
  • Models, Psychological
  • Needs Assessment
  • Nursing Education Research
  • Predictive Value of Tests
  • Principal Component Analysis
  • Remedial Teaching / organization & administration*
  • Social Support
  • Southeastern United States
  • Student Dropouts / education*
  • Student Dropouts / psychology
  • Student Dropouts / statistics & numerical data
  • Students, Nursing* / psychology
  • Students, Nursing* / statistics & numerical data
  • Time Factors