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Description
While the concept of healthcare is largely respected in Arab culture, the stigma underlying mental health is particularly startling. This study examined the differences in mental health treatment-seeking behaviors using data from Arabs living in Syria (12.9%) and Arabs (25.6%) and non-Arabs (61.5%) living in the United States of ages

While the concept of healthcare is largely respected in Arab culture, the stigma underlying mental health is particularly startling. This study examined the differences in mental health treatment-seeking behaviors using data from Arabs living in Syria (12.9%) and Arabs (25.6%) and non-Arabs (61.5%) living in the United States of ages 18-60. A Web-based survey was developed to understand how factors like religiosity, acculturation, and positive attitudes towards psychological treatment increased help-seeking behaviors. This survey was also provided in Arabic to include non-English speaking participants. It was hypothesized that Arab-American individuals will be more open to pursuing professional psychological help when suffering from mental symptomology (i.e. anxiety) than individuals who identified as Syrian-Arabs. In contrast, both Syrian-Arabs and Arab-Americans would definitely pursue professional help when suffering from physical symptomology (i.e. ankle sprain). Striking differences were found based on Western acculturation. Findings suggested that Arab-Americans were less inclined towards treatment and more trusting of an in-group physician ("Dr. Ahmed") whereas Syrian-Arabs were more inclined to pursue psychological treatment and preferred to trust an out-group physician ("Dr. Smith"). The results of this study identify main concerns regarding Arab attitudes towards seeking mental health treatment, which can better inform future research and mental health services for this minority.
ContributorsRayes, Diana S (Author) / Brewer, Gene (Thesis director) / Cohen, Adam (Committee member) / Olive, Michael Foster (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2015-05
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Description
Trust was measured for a target profile that varied the target's religion and costly signaling behavior. Subjects were primed with a threat, romance, or neutral response previous to viewing the profile to determine if this had any effect on their trust ratings of the target. Participants were drawn from MTurk

Trust was measured for a target profile that varied the target's religion and costly signaling behavior. Subjects were primed with a threat, romance, or neutral response previous to viewing the profile to determine if this had any effect on their trust ratings of the target. Participants were drawn from MTurk with ages ranging from 18 to 75 (M= 33.2) and various religious backgrounds (including 210 Christians, 190 atheists/agnostics, and 92 other religious believers). Participants were presented with the threat, romance, or neutral vignette, shown the target profile, and asked to rate the target's trustworthiness. There was no main effect of the vignette condition (p = .088) or costly signaling (p = .099) on the target's trustworthiness. There was a main effect of target religion (p = .006) wherein the Muslim target was trusted more than the Catholic target. These findings do not replicate previous findings on religion, costly signaling, and trust.
ContributorsBesaw, Courtney Michelle (Author) / Cohen, Adam (Thesis director) / Brewer, Gene (Committee member) / School of Human Evolution and Social Change (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description

We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones

We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.

ContributorsBarolli, Adeiron (Author) / Jimenez Arista, Laura (Thesis director) / Wilson, Jeffrey (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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ContributorsCarnesi, Gregory (Author) / O'Rourke, Holly (Thesis director) / Brewer, Gene (Committee member) / Corbin, William (Committee member) / Chassin, Laurie (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2022-05
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ContributorsCarnesi, Gregory (Author) / O'Rourke, Holly (Thesis director) / Brewer, Gene (Committee member) / Corbin, William (Committee member) / Chassin, Laurie (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2022-05