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Given the importance of understanding how gender bias impacts the advancement of women into upper leadership, this second-order meta-analysis attempts to explore and summarize previous developments in the gender and leadership literature in order to present the current state of the literature and identify a roadmap for future research.
This dissertation delivers three primary theoretical contributions. First, I conducted a systematic review of the leadership and gender literature to create a primer, with relevant definitions and theoretical frameworks, for gender and leadership theory. This review highlighted that little theoretical integration exists to synthesize the literature on gender and leadership.
Second, I present a second-order meta-analysis and subsequent relative weights analysis to demonstrate the relationship between personality, gender and follower evaluation of leadership constructs such as leader emergence and other leader behaviors (ex: transformational leadership, ethical leadership, etc.). The final meta-analytic correlation matrix included 89 meta-analytic estimates (total k=1,404; total n=366,329). Results indicate that variation in the evaluation of leaders can be explained by gender, however, the subsequent relative weights analysis indicates that for no construct is gender the dominant predictor.
Finally, this dissertation presents a research agenda based on the current findings that will advance the field, including research questions ranging from resolving methodological issues related to the measurement of evaluations of behaviors rather than actual behaviors, to further understanding further moderators of the relationship between gender and leader evaluations.
Technology Usage has become an important topic because of disruptions in the healthcare space in recent years. Long wait times and service delivery times, reduced patient engagement and interaction on care plans, lack of access to just-in-time healthcare records, diverse IT infrastructure with little or no interoperability and frequent server downtimes are some of the critical issues that could leverage current technology solutions to engage with patients before, during and post encounter visits and significantly improve outcomes and patient experiences.
Objective – This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT2) model by examining the role of technology use in influencing healthcare outcomes and patient experience. The study also investigates how healthcare service modality (Brick& Mortar, Hospital at Home, Mobile/Telehealth) influences the relationship between technology use and patient outcomes. Specifically looking at how different healthcare modality types can leverage modern technologies and disruption trends to improve patient satisfaction and patient engagement throughout the life cycle of a patient encounter visit.
Methods – Our proposed model incorporates five constructs- Two Independent variables- Individual Behavioral Intention and Technology use, Two moderators- Cloud based Electronic Medical Record (EHR) and Modality and finally two dependent variables- Patient Satisfaction and Patient Engagement.
Results -Results from an online survey on technology use from patients who have experienced services from the different healthcare modalities will be collected and analyzed to support the model.
Conclusions – This study is an important innovative addition for the UTAUT2 model. It will have practical implications for academia and industry by informing future research and operationalization strategies on possible trends that could be leveraged to significantly improve performance and outcomes in the healthcare industry.
Key Words – Healthcare Business Types, Technology Use, Cloud Enabled Patient Portal, Healthcare Service Delivery, Technology Acceptance Model (UTAUT2), Patient Satisfaction, Patient Engagement
In recent years, Wide Bandgap (WBG) semiconductor based power devices has matured rapidly and are playing a significant role in high switching frequency power electronic applications. WBG materials such as silicon carbide (SiC) and gallium nitride (GaN) possess a higher critical breakdown strength, an increased thermal conductivity, and a wider energy bandgap than silicon which make WBG semiconductors as a material of choice in low on-resistance, high blocking voltage, high switching frequency and high operating temperature power applications. In addition, using these devices result in the overall size reduction of the devices as higher doping levels can be achieved at similar voltage levels.
A gate driver acts as an interface between power devices and logic-level control signals and plays a significant role in the switching behaviour of WBG devices. To increase the overall efficiency and reduce the footprint of the system high switching frequency operation of the devices is desirable. However, power consumption in the gate driving circuit increases with frequency. A viable strategy to reduce the gate driving power consumption is to use resonant gate driving technique where part of the energy stored in the gate capacitance is recycled.
In this dissertation, a novel resonant gate driver (RGD) for WBG devices is proposed which drives the semiconductor device using quasi-square wave by utilizing higher order harmonics. Firstly, the operating principles of the proposed gate driver circuit is presented. Secondly, a detailed characteristic analysis and power loss analysis of the circuit are provided. Additionally, a comprehensive simulation study of the proposed circuit is introduced. Moreover, a prototype of the proposed RGD was built and tested. Experimental results demonstrate that the proposed gate driving technique can significantly reduce power consumption in the gate driver circuit in comparison to conventional gate driving techniques.
How statistics are wielded and presented in the real world cannot be separated from the fact that social issues operate within systems of marginalization, privilege, and power. Thus, statistical literacy necessitates the application of a true critical lens. Continued calls for critical statistical literacy from a consumer orientation within K-16 education, points to the need for research on how critical statistical literacy is enacted, particularly among the population of preservice mathematics teachers responsible for answering such calls. This study employed case study methodologies to gain deeper insight into how secondary preservice mathematics teachers enact Critical Statistical Literacy Habits of Mind (CSLHM) when making sense of data representations from the media. Critical Statistical Literacy Habits of Mind (CSLHM) are the thinking behaviors called upon to make sense of statistical messages with a specific focus on how the statistics and/or statistical message are used to uphold or dismantle structures of inequity. Findings reveal that preservice teachers emergently enact CSLHM. Some preservice teachers enact particular CSLHM robustly, although not habitually. Broader implications include the need to support preservice teachers’ development of CSLHM so that they can support their students to do the same.
There is a long history of limited nutrition education in advanced healthcare curricula. This leaves healthcare providers unprepared to use nutrition to treat diagnoses that have been shown to respond to dietary intervention. This project attempted to determine if the knowledge, attitudes and confidence about nutrition could be improved with a short course.
The sample consisted of seven students in master’s programs in the social work and nursing fields. Students completed an eight module virtual training on the Mediterranean diet and phytochemical specific prescribing strategies. Participants completed a pre- and post-assessment evaluation gauging knowledge, Likert scale questions evaluating confidence, and open-ended questions assessing attitudes toward implementing gained knowledge into practice.
Results showed the mean score for the pre-assessment was 60.39 (S.D.=14.12) and for the post-assessment was 65.58 (S.D.=9.32). The Wilcoxon Ranks Test shows significant improvement (p<0.05) for fifteen of the eighteen questions related to confidence in nutrition information. Open-ended attitude questions revealed that participants were interested in sharing the newly-learned nutrition information with community members and clients. Social work and nursing students may benefit from incorporating added nutrition education into their curricula. Further programming implementations in the area, including possible trial integration into curricula, are needed to further evaluate effectiveness.
Higher education leadership demographics contrast sharply with formulaic narratives of
college campuses filled with gender and racial diversity, especially in intercollegiate athletics.
Black women occupy only 2% of athletic director positions at predominantly White institutions
and are vastly underrepresented in other leadership roles (National Collegiate Athletic
Association, 2022). Often overlooked and underrepresented in current intercollegiate athletic
literature, this qualitative, narrative inquiry aimed to provide an opportunity to both examine the
experiences and magnify the voices of Black women within intercollegiate athletic leadership
and explore how they access leadership opportunities. Ten in-depth, semi-structured narrative
interviews were conducted with Black women representing all levels of administrative leadership
within Division I, II, and III collegiate programs. Findings revealed the work experiences of
these Black women in athletic leadership were impacted by their need to maintain their
uniqueness/individuality as Black women, the devaluing of Black womanhood in college athletic
workplaces, and their motivation for persistence/survival in the industry. Understanding the
experiences of Black women who were successful in navigating the leadership hierarchy is
useful for higher education leaders to examine and revise current hiring, training, and
professional development programs to create a more diverse and inclusive environment in
intercollegiate athletic leadership.
The resident advisor (RA) position is one of the most complex and important roles on college campuses in the United States. There are few other student staff positions that require the same level of training and have the same level of responsibilities. While these staff members serve many functions, a primary responsibility of RAs is to make their residents feel welcomed and valued in their community. Black students who take on the RA role at historically White institutions (HWIs) are charged with creating welcoming environments for students and making them feel valued in the community while often not feeling welcomed or valued themselves in these same environments due to the stereotypes and racism they encounter on their campuses. Navigating the RA role as a Black student may mean helping their residents develop their sense of belonging while simultaneously trying to find spaces where they feel they belong. This study is a qualitative phenomenological one that explored the lived experiences of Black students serving in the RA role at HWIs and where they found belonging and support in their residence life department. The study shares the stories of 10 students using interviews and photovoice. The findings of this study are broken down into five themes: (1) Motivation, (2) Isolation, (3) Stereotyping, (4) Labor, and (5) Underrepresentation. The words and images captured by the participants of the study are used to better understand these themes and the needs of Black RAs.
AMY BIANG. Examining The Lived Experiences Of Counselors Of Color In The Eating Disorder Field: A Post-Intentional Phenomenological Study. (Under the direction of DR. CLARE MERLIN-KNOBLICH)
Though eating disorders (EDs) affect a diverse population, among professionals who treat EDs, Counselors of Color (COC) are under-represented (Jennings- Mathis et al., 2020). Because the ED field is predominately comprised of White professionals (AED, 2022; Jennings-Mathis et al., 2020), a danger exists that White invisibility hinders counselors and researchers from recognizing oppression and injustices that occur in the ED field. The purpose of this Post-Intentional Phenomenological study was to bring awareness of the experiences of COC in the ED field and create a dialogue for systemic and social change related to their experiences. Eleven participants were interviewed, and the interview material was analyzed using a post-intentional phenomenological design. Tentative manifestations, provocations, and productions emerged through a whole-part-whole analysis. Five tentative manifestations; unprepared, belonging, unspoken knowing, exhaustion, and microaggressions; seven provocations; vulnerability, race as an asset, complexity, sense of duty, nonmaleficence, double bind, and credibility, and two productions; cultural inclusion and fulfillment, offer valuable knowledge about the experiences of COCs in the eating disorder field. Implications for counselor education and the ED profession are discussed, along with limitations and future research considerations.
As student demographics continue to evolve in higher education, the intersection of college-going generation status and U.S. immigrant-generation status needs to be considered; particularly, how these bi/polycultural experiences may play a role in the experience of belonging in college. This qualitative, phenomenological research study aimed to explore the lived experiences of four-year university students identifying as first-generation in college (FGC) and second-generation immigrants (SGI) in the U.S., with a focus on sense of belonging experiences at this identity intersection. Ten in-depth, semi-structured interviews with FGC-SGI students at an institution in the Southeastern U.S. were conducted to highlight identity and belonging narratives. Findings revealed that FGC-SGI students more readily engaged in identity disclosure with peers who shared at least one identity, and experienced implicit pressures to succeed due to their identities. Findings also revealed that FGC-SGI students sought out peers with similar identity characteristics to feel belonging, and that belonging played a critical role in how they viewed both their identities and their abilities to succeed in college. Thus, understanding FGC-SGI students' lived experiences may assist equity-minded higher education professionals with framing and creating settings that purposefully engage with this population to promote a positive, inclusive college experience, aiding in persistence and success.
The focus of this dissertation is on the topic of chatter avoidance during machining and the prediction of specific cutting forces and maximum tool temperatures during machining using machine learning. Self-excited regenerative vibration or “chatter” is a significant obstacle in machining which results in poor surface quality. To avoid chatter, a 2D diagram of the depth cut limit vs. the spindle speed, called Stability Lobe Diagram (SLD), is used. The SLD depends on the cutting parameters and structural dynamics parameters. Theoretically, chatter can be avoided using the physic-based SLD. But in practice, there is a gap between the empirical results and what the theory supports due to the uncertainties associated with the in-process structural dynamics parameters. using a multivariate Newton method, given the empirical data sets. The first part of the dissertation focuses on the inverse problem in chatter avoidance where the in-process structural dynamics parameters are extracted using a multivariate Newton method, given the empirical data sets. The SLD and the cutting parameters are assumed to be known and given. Using this knowledge, the structural dynamics parameters are obtained using the inverse approach. In addition, the uncertainty in the value of each structural dynamics parameter derived through the inverse approach is also presented. The results derived from the algorithm are used to discover the sensitivity of the boundary with respect to each parameter. The last part of the dissertation covers the prediction of the specific cutting force and the maximum tool temperature during machining using machine learning models.
