Matching Items (275)
Description
Breastfeeding has been shown to dramatically improve health outcomes for both infants and mothers. Despite recommendations by almost all world and national health organizations to breastfeed exclusively for 6 months and to continue breastfeeding for one year, this goal is not met by the majority of women in the United States for multiple reasons. Health professionals, including physicians and nurses, can play a major role in educating and influencing mothers about breastfeeding, especially for women with comorbidities, taking medications, or undergoing medical procedures. An online survey was created to evaluate healthcare professionals' breastfeeding knowledge and opinions at a large hospital in Phoenix, Arizona using QuestionPro software. This survey was distributed for three weeks to the nursing and physician departments at the hospital in primarily the obstetric and post partum units. Of the seventy-nine individuals who completed the survey, the respondents were primarily female obstetric nurses. Respondents recognized the benefits of breastfeeding for both mother and infant, believed health professionals can influence the decision to breastfeed, and found a lack of support was the number one reason women discontinue breastfeeding. Despite this information, it is apparent from this survey, and similar studies in the past, that there are significant gaps in knowledge especially when it comes to contraindications to breastfeeding, medications used while breastfeeding, fluid intake during breastfeeding, and foods that can be consumed while breastfeeding. Additionally, the majority of the nurses who completed this survey did not believe their schooling adequately trained them in breastfeeding education and hands-on practice. This information could be used in future studies to guide breastfeeding education for nurses, physicians, and other health care professionals at the stated hospital and other facilities across the nation.
ContributorsConstenius, Lindsey Bowes (Author) / Bever, Jennie (Thesis director) / Kelly, Lesly (Committee member) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a probabilistic analysis to describe the variation between replicates of the experimental process, and analyze reliability of a structural system based on that model. In order to help design the EDP software to perform the full analysis, the probabilistic and regression modeling aspects of this analysis have been explored. The focus has been on creating and analyzing probabilistic models for the data, adding multivariate and nonparametric fits to raw data, and developing computational techniques that allow for these methods to be properly implemented within EDP. For creating a probabilistic model of replicate data, the normal, lognormal, gamma, Weibull, and generalized exponential distributions have been explored. Goodness-of-fit tests, including the chi-squared, Anderson-Darling, and Kolmogorov-Smirnoff tests, have been used in order to analyze the effectiveness of any of these probabilistic models in describing the variation of parameters between replicates of an experimental test. An example using Young's modulus data for a Kevlar-49 Swath stress-strain test was used in order to demonstrate how this analysis is performed within EDP. In order to implement the distributions, numerical solutions for the gamma, beta, and hypergeometric functions were implemented, along with an arbitrary precision library to store numbers that exceed the maximum size of double-precision floating point digits. To create a multivariate fit, the multilinear solution was created as the simplest solution to the multivariate regression problem. This solution was then extended to solve nonlinear problems that can be linearized into multiple separable terms. These problems were solved analytically with the closed-form solution for the multilinear regression, and then by using a QR decomposition to solve numerically while avoiding numerical instabilities associated with matrix inversion. For nonparametric regression, or smoothing, the loess method was developed as a robust technique for filtering noise while maintaining the general structure of the data points. The loess solution was created by addressing concerns associated with simpler smoothing methods, including the running mean, running line, and kernel smoothing techniques, and combining the ability of each of these methods to resolve those issues. The loess smoothing method involves weighting each point in a partition of the data set, and then adding either a line or a polynomial fit within that partition. Both linear and quadratic methods were applied to a carbon fiber compression test, showing that the quadratic model was more accurate but the linear model had a shape that was more effective for analyzing the experimental data. Finally, the EDP program itself was explored to consider its current functionalities for processing data, as described by shear tests on carbon fiber data, and the future functionalities to be developed. The probabilistic and raw data processing capabilities were demonstrated within EDP, and the multivariate and loess analysis was demonstrated using R. As the functionality and relevant considerations for these methods have been developed, the immediate goal is to finish implementing and integrating these additional features into a version of EDP that performs a full streamlined structural analysis on experimental data.
ContributorsMarkov, Elan Richard (Author) / Rajan, Subramaniam (Thesis director) / Khaled, Bilal (Committee member) / Chemical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Ira A. Fulton School of Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
DescriptionThe goal of this study is to explore the relationship between breastfeeding, postpartum depression and postpartum weight at 1 and 6 months.
ContributorsFlowers, Jenna (Author) / Reifsnider, Elizabeth (Thesis director) / Bever, Jennie (Committee member) / Moramarco, Michael (Committee member) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
The purpose of the study was to determine the level and type of public policy involvement among registered nurses (RN) who are members of the Arizona Nurses Association (AzNA). Furthermore, the aim of the study was to identify the knowledge base and motivation of nurses and their involvement in public policy as well as the barriers and benefits. A 20- item survey was sent to all of the members of AzNA. There were 39 responses used in the analysis. The highest reported public policy activities in which the nurses had participated were: voted (90%), contacted a public official (51%), and gave money to a campaign or for a public policy concern (46%). Lack of time was the most frequently reported barrier to involvement and improving the health of the public was the most frequently reported benefit to involvement. The number of public policy education/information sources and the highest level of education positively correlate to the nurses' total number of public policy activities (r = .627 p <0.05; r = .504, p <0.05). Based on the results of stepwise linear regression analysis, the participants' age, number of education/information sources, and efficacy expectation predict 68.8% of involvement in public policy activities. The greater the number of education/information sources, the greater the number of public policy activities nurses report having participated in.
ContributorsHartman, Mykaila Corrine (Author) / Stevens, Carol (Thesis director) / Munoz, Aliria (Committee member) / Link, Denise (Committee member) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
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 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.
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