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1.
J Am Assoc Nurse Pract ; 35(2): 135-141, 2023 Feb 01.
Article in English | MEDLINE | ID: covidwho-2309365

ABSTRACT

ABSTRACT: This article highlights the development and implementation of interactive training experiences for graduate nursing students as part of specialty training in endocrinology. Emphasis was placed on accomplishing the shift from on-campus to virtual training while maintaining fidelity and student satisfaction. A total of 106 graduate nursing students from five cohorts submitted evaluations. Student satisfaction remained high regardless of whether the content was delivered in person or virtually. Most students in the virtual cohorts evaluated the online training positively. Student presentation grades were highest with on-campus delivery. Transitioning in-person training to a virtual environment can be an effective method of delivering nurse practitioner education while promoting student satisfaction. Recommendations for optimizing hybrid learning experiences are offered based on adult learning principles.


Subject(s)
Education, Nursing, Graduate , Nurse Practitioners , Students, Nursing , Adult , Humans , Learning , Nurse Practitioners/education , Personal Satisfaction
2.
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.11.19.21266469

ABSTRACT

Type 2 diabetes mellitus (T2D) presents a major health and economic burden that could be alleviated with improved early prediction and intervention. While standard risk factors have shown good predictive performance, we show that the use of blood-based DNA methylation information leads to a significant improvement in the prediction of 10-year T2D incidence risk. Previous studies have been largely constrained by linear assumptions, the use of CpGs one-at-a-time, and binary outcomes. We present a flexible approach (via an R package, MethylPipeR ) based on a range of linear and tree-ensemble models that incorporate time-to-event data for prediction. Using the Generation Scotland cohort (training set n cases =374, n controls =9,461; test set n cases =252, n controls =4,526) our best-performing model (Area Under the Curve (AUC)=0.872, Precision Recall AUC (PRAUC)=0.302) showed notable improvement in 10-year onset prediction beyond standard risk factors (AUC=0.839, PRAUC=0.227). Replication was observed in the German-based KORA study (n=1,451, n cases = 142, p=1.6×10 -5 ).


Subject(s)
Diabetes Mellitus, Type 2
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