Roman Gretchen, Buman Matthew Paul
Physical Therapy, Midwestern University, Glendale, AZ, USA.
College of Health Solutions, Arizona State University, Phoenix, AZ, USA.
J Educ Eval Health Prof. 2019;16:5. doi: 10.3352/jeehp.2019.16.5. Epub 2019 Feb 26.
The field of physical therapy education is seeking an evidence-based approach to inform qualified applicant admission. A considerable amount of research exists; however, results represent an array of outcomes making practical application difficult. It aims to determine what preadmission criteria are predictors of graduation success.
Data from the 2013-2016 graduating cohorts (n=149) were collected. Predictors included verbal, quantitative and analytical Graduate Record Examination rank percentile), admissions interview, precumulative science grade point average (SGPA), precumulative grade point average (UGPA), and reflective essay. Measures of graduation success were identified as the National Physical Therapy Examination (NPTE) and grade point average at the time of graduation (GGPA). Two separate mixed effects models determined associations between preadmission predictors and NPTE, and preadmission predictors and GGPA.
Overall, NPTE model fit comparison was significant (df=10; p=0.001) and within-cohort variance decreased 59.5%. The NPTE was associated with GGPA (β=125.21; p=0.001), and verbal Graduate Record Examination (VGRE) rank percentile interview, essay and GGPA (p≤0.001) impacted model fit. Overall, GGPA model fit comparison was not significant (df=8; p=0.56) and within-cohort variance was decreased by 16.4%. The GGPA was associated with interview (β=0.02; p=0.04) and UGPA (β=0.25; p=0.04). VGRE rank percentile, interview, UGPA, and essay (p≤0.02) impacted model fit.
Above findings suggest that GGPA predicts NPTE, and the interview and UGPA predict GGPA. The essay and VGRE rank percentile warrant attention because of their influence on model fit. It is recommended that admissions ranking matrices designate greater weight to the interview, UGPA, VGRE rank percentile, and essay.
物理治疗教育领域正在寻求一种基于证据的方法来指导合格申请人的录取。现有大量研究;然而,研究结果呈现出一系列结果,使得实际应用变得困难。其目的是确定哪些入学前标准是毕业成功的预测因素。
收集了2013 - 2016届毕业生队列(n = 149)的数据。预测因素包括语言、定量和分析性研究生入学考试排名百分位、入学面试、预累积科学平均绩点(SGPA)、预累积平均绩点(UGPA)以及反思性文章。毕业成功的衡量指标被确定为国家物理治疗考试(NPTE)和毕业时的平均绩点(GGPA)。两个独立的混合效应模型确定了入学前预测因素与NPTE之间以及入学前预测因素与GGPA之间的关联。
总体而言,NPTE模型拟合比较具有显著性(自由度 = 10;p = 0.001),队列内方差下降了59.5%。NPTE与GGPA相关(β = 125.21;p = 0.001),并且语言研究生入学考试(VGRE)排名百分位、面试、文章和GGPA(p≤0.001)影响模型拟合。总体而言,GGPA模型拟合比较不具有显著性(自由度 = 8;p = 0.56),队列内方差下降了16.4%。GGPA与面试(β = 0.02;p = 0.04)和UGPA(β = 0.25;p = 0.04)相关。VGRE排名百分位、面试、UGPA和文章(p≤0.02)影响模型拟合。
上述研究结果表明,GGPA可预测NPTE,面试和UGPA可预测GGPA。文章和VGRE排名百分位因其对模型拟合的影响而值得关注。建议招生排名矩阵给予面试、UGPA、VGRE排名百分位和文章更大的权重。