Impact of COVID-19 on Nursing Students: Factors Associated with PTSD Risk.

The COVID-19 pandemic rapidly transformed nursing education, creating multiple stressors for students. The researchers in this study examined how undergraduate and graduate nursing students at a faith-based university perceived the impact of COVID-19. A multiple linear regression model explored the effect of 17 variables on the Impact of Events Scale-Revised (IES-R) scores.

Utilizing timed categorical recall (naming US cities) for rapid bedside dementia screening.

The availability of fast validated screening for dementia is a critical clinical need to improve neurologic examination time efficiency. This study validated a 1-minute timed categorical recall (TCR) method, naming as many US cities as possible and compared TCR to the Folstein Minimental Status Exam (MMSE) as a preliminary cognitive screening tool. Random uncompensated 349 volunteers were recruited ages 18 to 97 from local free clinics, retirement homes, university faculty, and students in Lynchburg, Virginia 2015 to 2020.

Hybrid Threshold-Based Sequential Procedures for Detecting Compromised Items in a Computerized Adaptive Testing Licensure Exam.

Using classical test theory and item response theory, this study applied sequential procedures to a real operational item pool in a variable-length computerized adaptive testing (CAT) to detect items whose security may be compromised. Moreover, this study proposed a hybrid threshold approach to improve the detection power of the sequential procedure while controlling the Type I error rate. The hybrid threshold approach uses a local threshold for each item in an early stage of the CAT administration, and then it uses the global threshold in the decision-making stage.

Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments.

This article provides a review of Bayesian model averaging as a means of optimizing the predictive performance of common statistical models applied to large-scale educational assessments. The Bayesian framework recognizes that in addition to parameter uncertainty, there is uncertainty in the choice of models themselves. A Bayesian approach to addressing the problem of model uncertainty is the method of Bayesian model averaging.