Research article summary (published 30 May 2008):

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

Full Abstract

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.

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Author information

Author/s: Hopkins, T Hampton (TH);

Affiliation: Student Services and Enrollment Management, Carolinas College of Health Sciences, 1200 Blythe Boulevard, Charlotte, NC 28203, USA. hampton.hopkins(-atsign-)carolinascollege.edu

Journal and publication information

Publication Type: Journal Article

Journal: The Journal of nursing education (J Nurs Educ), published in United States. (Language: eng)

Reference: 2008-Jun; vol 47 (issue 6) : pp 254-9

Dates: Created 2008/06/18; Completed 2008/07/31;

PMID: 18557312, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

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; psychology; statistics & numerical data Students, Nursing - psychology; statistics & numerical data Time Factors

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