Matching Items (312)
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Description
Objectives: To explore the feasibility and effects of using a meditation mobile app 10-minutes a day for 4-weeks to reduce burnout (primary outcome), improve mindfulness, reduce stress, and depression in physician assistant (PA) students compared to a wait-list control.
Methods: This study was a randomized, wait-list, control trial with assessments

Objectives: To explore the feasibility and effects of using a meditation mobile app 10-minutes a day for 4-weeks to reduce burnout (primary outcome), improve mindfulness, reduce stress, and depression in physician assistant (PA) students compared to a wait-list control.
Methods: This study was a randomized, wait-list, control trial with assessments at baseline and post-intervention (week 4). Participants were asked to meditate using Calm for 10 minutes per day. A p value ≤0.05 was considered statistically significant.
Results: The majority of participants (n=19) stated using Calm helped them cope with the stress of PA school. The intervention group participated in meditation for an average of 76 minutes/week. There were significant differences in all outcomes for the intervention group (all p ≤0.06). There was a significant interaction between group and time factors in emotional exhaustion (p=.016) and depersonalization (p=.025).
Conclusions: Calm is a feasible way to reduce burnout in PA students. Our findings provide information that can be applied to the design of future studies.
ContributorsWorth, Taylor Nicole (Author) / Huberty, Jennifer (Thesis director) / Will, Kristen (Committee member) / Puzia, Megan (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Description
Glioblastoma multiforme (GBMs) is the most prevalent brain tumor type and causes approximately 40% of all non-metastic primary tumors in adult patients [1]. GBMs are malignant, grade-4 brain tumors, the most aggressive classication as established by the World Health Organization and are marked by their low survival rate; the median

Glioblastoma multiforme (GBMs) is the most prevalent brain tumor type and causes approximately 40% of all non-metastic primary tumors in adult patients [1]. GBMs are malignant, grade-4 brain tumors, the most aggressive classication as established by the World Health Organization and are marked by their low survival rate; the median survival time is only twelve months from initial diagnosis: Patients who live more than three years are considered long-term survivors [2]. GBMs are highly invasive and their diffusive growth pattern makes it impossible to remove the tumors by surgery alone [3]. The purpose of this paper is to use individual patient data to parameterize a model of GBMs that allows for data on tumor growth and development to be captured on a clinically relevant time scale. Such an endeavor is the rst step to a clinically applicable predictions of GBMs. Previous research has yielded models that adequately represent the development of GBMs, but they have not attempted to follow specic patient cases through the entire tumor process. Using the model utilized by Kostelich et al. [4], I will attempt to redress this deciency. In doing so, I will improve upon a family of models that can be used to approximate the time of development and/or structure evolution in GBMs. The eventual goal is to incorporate Magnetic Resonance Imaging (MRI) data into a parameterized model of GBMs in such a way that it can be used clinically to predict tumor growth and behavior. Furthermore, I hope to come to a denitive conclusion as to the accuracy of the Koteslich et al. model throughout the development of GBMs tumors.
ContributorsManning, Miles (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Preul, Mark (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2012-12