Early identification of at-risk nursing students: a student support model.
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.
Hopkins
T Hampton
TH
Student Services and Enrollment Management, Carolinas College of Health Sciences, 1200 Blythe Boulevard, Charlotte, NC 28203, USA. hampton.hopkins@carolinascollege.edu
D000293:Adolescent; D000328:Adult; D000704:Analysis of Variance; D003071:Cognition; D004507:Education, Nursing, Associate / Q000458:organization & administration*; D004521:Educational Measurement; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D017145:Models, Educational; D016294:Models, Nursing; D008960:Models, Psychological; D020380:Needs Assessment; D015402:Nursing Education Research; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D012073:Remedial Teaching / Q000458:organization & administration*; D012944:Social Support; D015144:Southeastern United States; D013332:Student Dropouts / Q000193:education* / Q000523:psychology / Q000706:statistics & numerical data; D013338:Students, Nursing / Q000523:psychology / Q000706:statistics & numerical data; D013997:Time Factors