在2个美国加速混合物理治疗项目的医生的国家物理治疗考试成功的预测因素

Breanna Reynolds, Casey A Unverzagt, A. Koszalinski, R. Gatlin, J. Seale, Kendra Gagnon, Kareaion Eaton, S. Koppenhaver
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引用次数: 3

摘要

导读:虽然在以前的物理治疗博士(DPT)教育中已经报道了成功的预测因素,但DPT教育的加速混合模型尚未包括在内,可能会产生不同的结果。本研究的目的是研究2个美国认可的加速混合DPT项目中国家物理治疗检查(NPTE)表现的预测因素。文献综述:目前研究NPTE成功预测因素的证据包括入学数据(本科或先决条件的平均成绩[GPA]和研究生入学考试[GRE])、DPT项目GPA、非认知变量和项目变量。虽然这些变量与NPTE成功的关系的结果是不一致的,但DPT项目的GPA似乎是最一致的发现。研究对象:2个加速混合DPT项目2019年和2020年毕业班的学生记录(共4个班,n = 355)。方法:评估入院前、非认知、计划内和NPTE准备变量,以确定它们与NPTE评分的关系。使用双变量相关性评估每个变量与NPTE评分之间的关联。采用逐步线性回归分析确定最简洁的预测模型。在确定连续水平变量的最佳预测阈值后,计算每个变量与第一次尝试通过NPTE之间的比值比。结果:入院前、计划内和NPTE准备变量与NPTE评分存在统计学显著相关性;相关性最强的是中点GPA (r = 0.576, P < 0.05)。最简洁的预测模型包括中点GPA、第一次通过实践考试和评估工具(PEAT);二分类结果、及格分数≥600)、Scorebuilders在线优势考试分数、GRE综合百分比,占NPTE分数方差的52.6%。第一次通过PEAT考试的人第一次通过NPTE考试的几率是第一次通过PEAT考试的人的14倍,而平均分至少为3.35的学生的几率是第一次通过NPTE考试的8倍。讨论与结论:中点课程GPA是NPTE分数的最强预测因子,其次是PEAT和在线优势(2个NPTE准备变量)。综合GRE百分比是唯一有助于预测NPTE分数的录取变量。这些发现与之前在其他DPT教育模式中检验NPTE成功预测因素的研究没有什么不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictors of Success on the National Physical Therapy Examination in 2 US Accelerated-Hybrid Doctor of Physical Therapy Programs
Introduction: Although predictors of success have been reported within Doctor of Physical Therapy (DPT) education previously, accelerated-hybrid models of DPT education have not been included and may yield different results. The purpose of this study was to examine predictors of National Physical Therapy Examination (NPTE) performance within 2 US-accredited accelerated-hybrid DPT programs. Review of Literature: Current evidence examining predictors of NPTE success have included admissions data (undergraduate or prerequisite grade point average [GPA] and Graduate Record Examination [GRE]), DPT program GPA, noncognitive variables, and program variables. Although results of the relationship of these variables to NPTE success are inconsistent, DPT program GPA seemed to be the most consistent finding. Subjects: Student records from the 2019 and 2020 graduating classes of 2 accelerated-hybrid DPT programs (4 classes total, n = 355). Methods: Preadmission, noncognitive, within-program, and NPTE preparatory variables were assessed to determine their relationship with NPTE score. The association between each variable and NPTE score was assessed using bivariate correlations. The most parsimonious predictive model was determined using stepwise linear regression analysis. After determining the best predictive threshold for continuous level variables, odds ratios were calculated between each variable and passing the NPTE on the first attempt. Results: Statistically significant associations with NPTE score were noted with most preadmission, within-program, and NPTE preparatory variables; the strongest association was midpoint program GPA (r = 0.576, P < .05). The most parsimonious predictive model consisted of midpoint program GPA, first attempt passing Practice Exam & Assessment Tool (PEAT; dichotomous outcome, passing score ≥600), Scorebuilders Online Advantage examination score, and combined GRE percentage, which accounted for 52.6% of the variance in NPTE scores. The odds of passing NPTE on the first try were 14 times higher in individuals who passed the PEAT examination on the first try and 8 times higher in students with a midpoint GPA of at least 3.35. Discussion and Conclusion: Midpoint program GPA was the strongest predictor of NPTE score, followed by the PEAT and Online Advantage (2 NPTE preparatory variables). Combined GRE percentage was the only admissions variable contributing to the prediction of NPTE score. These findings are not different from previous research examining predictors of NPTE success in other DPT education models.
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