Showing 1 - 4 of 4 Research Library Publications
Posted: | Hilary Barnes, Asefeh Faraz Covelli, Jonathan D. Rubright

Research in Nursing & Health: Volume 46, Issue 1, Pages 127-135

 

As interest in supporting new nurse practitioners' (NPs) transition to practice increases, those interested in measuring the concept will need an instrument with evidence of reliability and validity. The Novice NP Role Transition (NNPRT) Scale is the first instrument to measure the concept. Using a cross-sectional design and data from 210 novice NPs, the purpose of this study was to confirm the NNPRT Scale's internal factor structure via confirmatory factor analysis (CFA).

Posted: | Erfan Khalaji, Sukru Eraslan, Yeliz Yesilada, Victoria Yaneva

Behavior & Information Technology

 

This study builds upon prior work in this area that focused on developing a machine-learning classifier trained on gaze data from web-related tasks to detect ASD in adults. Using the same data, we show that a new data pre-processing approach, combined with an exploration of the performance of different classification algorithms, leads to an increased classification accuracy compared to prior work.

Posted: | Monica M. Cuddy, Chunyan Liu, Wenli Ouyang, Michael A. Barone, Aaron Young, David A. Johnson

Academic Medicine: June 2022

 

This study examines the associations between Step 3 scores and subsequent receipt of disciplinary action taken by state medical boards for problematic behavior in practice. It analyzes Step 3 total, Step 3 computer-based case simulation (CCS), and Step 3multiple-choice question (MCQ) scores.

Posted: | Victoria Yaneva, Brian E. Clauser, Amy Morales, Miguel Paniagua

Advances in Health Sciences Education: Volume 27, p 1401–1422

 

After collecting eye-tracking data from 26 students responding to clinical MCQs, analysis is performed by providing 119 eye-tracking features as input for a machine-learning model aiming to classify correct and incorrect responses. The predictive power of various combinations of features within the model is evaluated to understand how different feature interactions contribute to the predictions.