SOC Graduate Student Conference Papers

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    Reflections of milestones associated with the transition to adulthood during COVID-19 pandemic
    (Wichita State University, 2023-04-14) Baus, Desiree; Hertzog, Jodie
    In response to the novel COVID-19 virus, many state governments imposed stay at home orders that dramatically altered the ways in which institutions operate, forcing college students to adapt to different modes of communication, ways in which they completed coursework, and changes in dwelling locations. This study examined qualitative data obtained unobtrusively as course-required journal entries and with permission from the assigning professor and students. Five Illinois-based college students' journal entries from six weeks immediately following the mandated stay at home orders were explored to address how these adaptations affected their achievement of social-psychological milestones associated with the transition to adulthood. To explore how these adaptations manifested in their reflections of social experiences during the pandemic, 11 journal entries per participant were analyzed thematically in line with conceptual adult milestones associated with life course theory and identity development. Utilizing Microsoft Word and Excel, first order coding was completed "in vivo," and as categories emerged, conceptual frameworks were utilized to deductively organize codes into themes. Findings indicate that college students experienced hardships when adjusting to living in their parents' homes after having lived independently, felt socially isolated from friend and peer networks when separated from their college campus, and experienced struggles while adapting to the challenges of online learning. This study calls for more longitudinal research on the impacts of COVID-19 protocols on youths' transition to adulthood to better understand and predict generational differences.
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    Investigating experiences of joy among LGBTQ young adults
    (Wichita State University, 2023-04-14) Abeywickrama, Amanda; Pearson, Jennifer D.
    As social sciences tend to gravitate towards studying discrimination, stigma and other negative experiences faced by marginalized groups, scholarly studying of joy is vital to foster positivity in our communities. While studying disparities among LGBTQ youth contribute to improvements in access to education, social life, and healthcare, a singular focus on oppression creates a false narrative of suffering and despair. Affirming contexts such as LGBTQ- supportive schools have been associated with less victimization, lower suicide risk, and increased wellbeing. As a social phenomenon, joy is linked to community mobilization and sustained action, which can lead to improvements in cultural climate through changes in policies and resources. In this spirit, this study aimed to understand how LGBTQ young adults find joy in their sexual and gender identities. The current study consisted of 8 in-depth, semi- structured interviews, each lasting for approximately one hour, with participants between 18 and 28 years in the larger Wichita area who identified as a sexual and/or gender minority. Following a grounded theory approach, coding consisted of open, axial, and selective coding phases. During the iterative processes of axial and selective coding, emerging themes were identified. Preliminary results indicated that discovery and learning of LGBTQ identities, expressing authentic gender and sexuality, acceptance from self and family and friends, and feelings of community, belonging, and shared understanding elicited joy. Additionally, respondents described emotional experiences of joy, as "delight," "wave of happiness," and "relief," while physical sensations of joy were described as "electricity," "seeing colors," and "bubbly," despite participants' insecurities.
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    Obesity classification from facial images using deep learning
    (Wichita State University, 2021-04-02) Siddiqui, Hera; Siddiqui, Hera; Rattani, Ajita; Cure Vellojin, Laila N.; Woods, Nikki Keene; Lewis, Rhonda K.; Twomey, Janet M.; Smith-Campbell, Betty; Hill, Twyla J.
    INTRODUCTION: Obesity is a serious health problem that is on the rise both in the United States and globally. Obesity is frequently defined using the clinical Body Mass Index (BMI) ratio of height and weight. Overweight individuals have a BMI between 25-30, and those over 30 are classified as obese. Obesity can lead to heart disease, type 2 diabetes, and many other serious health conditions. Self-diagnostic face-based solutions are being investigated for obesity classification and monitoring. PURPOSE: To classify obesity status based on facial images using deep learning-based convolutional neural networks (CNNs). METHODS: The four CNNs (VGG16, ResNet50, DenseNet121, and MobileNetV2) used in this study were pre-trained on three public datasets (ImageNet, VGGFace, and VGGFace2). Using the above CNNs, we extracted deep features from the FIW-BMI and VisualBMI datasets annotated with BMI information. The deep features from 8298 images in the FIW-BMI dataset along with BMI values were then used to train a Support Vector Classification (SVC) classifier. The trained SVC model was tested on 4206 different images from the VisualBMI dataset for the validation. RESULTS: CNNs trained on ImageNet dataset obtained an initial accuracy (percentage of correct obese and non-obese classifications) in the range 64% to 72%. Accuracy of 84% to 86% was obtained by using CNNs trained on VGGFace dataset. 86% accuracy was obtained by concatenating features from pre-trained (VGGFace) and fine-tuned (FIW-BMI) model. ResNet-50 trained on VGGFace2 dataset obtained an accuracy of 91% when features from the original image datasets were used and 92% accuracy when features were fused from the original image with the horizontally flipped image. The fused image modifications resulted in a model with Sensitivity, Specificity, and Precision of 0.90, 0.94, and 0.95, respectively. Mean Absolute Error (MAE) of this model in predicting BMI is 3.16 and area under the curve (AUC) is 0.97. CONCLUSION: Obesity can be predicted from facial images using deep learning models with a promising accuracy. SVC models trained on deep features extracted from models pre-trained on VGGFace2 dataset performed better than models pre-trained on ImageNet dataset. ResNet-50 (pre-trained on VGGFace2) obtained the highest accuracy of 92% by combining features from the original image and horizontally flipped image. These models when deployed on smartphones can help individuals in monitoring their obesity status, BMI, and weight changes.
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    Reddit discussions of compassion fatigue
    (Wichita State University, 2021-04-02) Malone, Stormy; Hertzog, Jodie; Lewis, Rhonda K.
    Compassion fatigue is a serious concern for those expressing compassion chronically (Vu & Bodenmann, 2017). It occurs when the stress of expressing compassion leaves a person unable to continue to care for others (Ledoux, 2015). This issue results in emotional and physical symptoms of distress (Vu and Bodenmann, 2017). An approach for coping with compassion fatigue is to find and maintain a healthy support system. Reddit provides users with an opportunity to build a system of support through participation in subreddits, which are communities of Reddit users (Sowles, et al., 2017). In this study, the goal was to develop a greater understanding of compassion fatigue through an examination of how this concept is discussed across subreddit communities, and how these communities support their members who are experiencing symptoms of compassion fatigue. Content analysis was used to examine posts made in five subreddit communities, and patterns were identified across the groups. This analysis resulted in four themes: social contexts (discussion of relationships), psychological (the expression of negative emotions), work (any mention of work stress or environments), and supportive communication (words of encouragement, empathy, or advice). The conversations observed on Reddit showed that subreddit members discussed their experiences with compassion fatigue in the contexts of their personal lives, requested advice for coping from others, and provided each other with support. This research also indicated that subreddit communities are a valuable environment for receiving support for compassion fatigue.
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    Understanding the prevalence of gestational diabetes in urban and rural communities in Kansas
    (Wichita State University, 2021-04-02) Ali, Umama; Woods, Nikki Keene; Cure, Laila; Rattani, Ajita; Hill, Twyla J.
    INTRODUCTION: Gestational diabetes is one of the most common medical conditions women encounter during pregnancy and is the leading cause of maternal morbidity and mortality, perinatal and neonatal morbidity, and long-term consequences for both mother and baby. The percentage of pregnant women with gestational diabetes increased by 56% in the last ten years in the United States. Maternal obesity is a significant risk factor for gestational diabetes. In 2020, the Kansas obesity rate was 30-35%, which is expected to rise to more than 55% by 2030. Additionally, maternal and child health disparities are experienced to a greater extent in rural areas including obesity, in which rates are higher in rural counties than in large urban counties. It is hypothesized most rural counties in Kansas will have a higher prevalence of gestational diabetes. PURPOSE: Analyze the prevalence of gestational diabetes in rural and urban counties in Kansas. METHODS: Kansas Department of Health and Environment (KDHE) was used to gather diabetes and pre-pregnancy BMI data from 2005 to 2019. The data was analyzed by creating trend graphs and examining the prevalence in categorized peer groups. RESULTS: There is an increasing trend in rates and number of cases of gestational onset diabetes and pre-pregnancy BMI classified as overweight and obese. Urban counties have a higher prevalence of gestational diabetes, however rural counties have a higher prevalence of pre-pregnancy BMI overweight and obese cases. Rural counties have experienced a growing increase over the years in the prevalence of gestational diabetes. CONCLUSION: Similar national studies from the Centers for Disease Control and Prevention have reported an increased prevalence of gestational diabetes from 3.7% in 2012 to 5.8% in 2016. In Kansas gestational diabetes increased from 5.8% in 2012 to 6.4% in 2016. Observed increases in the prevalence of gestational diabetes could be reflective of recent increases in the prevalence of pre-pregnancy obesity that is experienced to a greater extent in rural communities. Future efforts to address this growing health disparity are needed.
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