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重新思考护理学校的入学标准。

Rethinking the admission criteria to nursing school.

机构信息

Centre for Medical and Health Sciences Education, University of Auckland, Private Bag 92019, Auckland, New Zealand.

出版信息

Nurse Educ Today. 2011 Nov;31(8):727-32. doi: 10.1016/j.nedt.2010.11.024. Epub 2010 Dec 17.

Abstract

The main objective of this study was to identify the best predictors for student achievements (Undergraduate Grade Point Average (UGPA)) in their first year in an undergraduate nursing programme. Data were acquired from the Tracking Project database which is held by the Faculty of Medical and Health Sciences at the University of Auckland. The data (n=134) included information on student demographics, final year secondary school achievements (National Certificate of Educational Achievement Grade Point Average (NCEAGPA) & NCEA Credits), university admission ranking scores, and achievements in first year in the undergraduate nursing programme (UGPA). Linear regression models were used to identify the best predictors for first year students' UGPA in the nursing programme. The regression models suggest that the best predictor for the first year GPA is the NCEAGPA (beta=.488; R(2)(for the entire model)=.53), followed by the admission ranking scores (beta=.308; R(2)=.40). Based on these findings, it is suggested that a Dual Admission Model (DAM) be utilised whereby students could be admitted either by the current university admission criteria or by an alternative model, which is purely based on the predictability of achievement within the nursing programme. Application of the DAM to other institutions/countries was discussed.

摘要

本研究的主要目的是确定预测学生在本科护理项目第一年学业成绩(平均绩点(UGPA))的最佳指标。数据来自奥克兰大学医学和健康科学学院的跟踪项目数据库。数据(n=134)包括学生人口统计学信息、高中最后一年的成绩(国家教育成就证书平均绩点(NCEAGPA)和 NCEA 学分)、大学入学排名分数以及本科护理项目第一年的成绩(UGPA)。线性回归模型用于确定护理项目中一年级学生 UGPA 的最佳预测指标。回归模型表明,对一年级 GPA 的最佳预测指标是 NCEAGPA(β=.488;整个模型的 R²(for the entire model)=.53),其次是入学排名分数(β=.308;R²=.40)。基于这些发现,建议采用双重入学模式(DAM),学生可以通过当前的大学入学标准或仅基于护理项目内可预测性的替代模式入学。讨论了将 DAM 应用于其他机构/国家的情况。

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