超越学术:在物理治疗教育中的成功的整体预测因素
Liana Wooten1,2,3,4,5,6,7, Lorenzo Casertano1,2,3,4,5,6,7, Edward P Mulligan1,2,3,4,5,6,7
1Liana Wooten is the Co-Director of Admissions and Assistant Professor in the Doctor of Physical Therapy Program, Department of Rehabilitation Sciences School of Medicine, Tufts University, 101 E Washington St, Suite 950, Phoenix, AZ 85004 (liana.wooten@tufts.edu). Please address all correspondence to Liana Wooten.
第一代大学生和那些在加速物理治疗教育计划 (PTEP) 中工作的人可能需要额外的支持来获得学术成功. 招生委员会可以使用本科GPA来确定有前途的候选人.
科学领域:
- 物理治疗教育教育 物理治疗教育
- 高等教育招生 高等教育招生
- 学生成功指标 学生成功指标
背景情况:
- 传统的学术指标是非加速物理治疗教育计划 (PTEPs) 成功的既定预测指标.
- 加速,混合PTEP带来了独特的挑战,可能需要评估不同的成功因素.
研究的目的:
- 探索非传统变量来预测早期学生在加速,混合PTEP的成功.
- 评估整体招生因素与学生成绩之间的关系.
主要方法:
- 在加速,混合PTEP中从两个队列中收集了入学前和计划中的数据.
- 利用相关性和回归来分析独立变量与第二学期 (cGPA2) 后的累积GPA之间的关联.
- 雇佣了t测试来根据第一代状态来比较学生的表现.
主要成果:
- 计划期间的第一代状态和工作状态解释了cGPA2.2的26%的差异.
- 无论是第一代状态 (r = -0.258,P = .047) 和在计划期间工作 (r = -0.422,P = .032) 都与cGPA2.2相反相关.
- 在本科GPA的最后60个学分 (u60GPA) 解释了cGPA2的12% (R2 = 0.124,P = .005).
结论:
- 第一代学生和在课程期间工作的学生可能需要有针对性的学术支持.
- 将u60GPA纳入招生审查可以加强加速,混合PTEP的选择过程.
- 对招生因素的整体审查对于识别可能成功的学生和使物理治疗工作人员多样化至关重要.
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