Matching Items (3)
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- All Subjects: Implicit Bias
- All Subjects: Teams
- Creators: Gutzwiller, Robert S.
- Creators: Allen, Angela
Description
Minority mental health patients face many health inequities and inequalities that may stem from implicit bias and a lack of cultural awareness from their healthcare providers. I analyzed the current literature evaluating implicit bias among healthcare providers and culturally specific life traumas that Latinos and African Americans face that can impact their mental health. Additionally, I researched a current mental health assessments tool, the Child and Adolescent Trauma Survey (CATS), and evaluated it for the use on Latino and African American patients. Face-to-face interviews with two healthcare providers were also used to analyze the CATS for its’ applicability to Latino and African American patients. Results showed that these assessments were not sufficient in capturing culturally specific life traumas of minority patients. Based on the literature review and analysis of the interviews with healthcare providers, a novel assessment tool, the Culturally Traumatic Events Questionnaire (CTEQ), was created to address the gaps that currently make up other mental health assessment tools used on minority patients.
ContributorsAldana, Lauren Michelle (Author) / Sullivan-Detheridge, Julie (Thesis director) / Allen, Angela (Committee member) / Edson College of Nursing and Health Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Social categories such as race and gender are associated by people with certain characteristics (e.g. males are angry), which unconsciously affects how people evaluate and react to a person of specific social categories. This phenomenon, referred to as implicit bias, has been the interest of many social psychologists. However, the implicit bias research has been focusing on only one social category at a time, despite humans being entities of multiple social categories. The research also neglects the behavioral contexts in which implicit biases are triggered and rely on a broad definition for the locus of the bias regulation mechanism. These limitations raise questions on whether the current bias reduction strategies are effective. The current dissertation sought to address these limitations by introducing an ecologically valid and multidimensional method. In Chapters 1 and 2, the mouse-tracking task was integrated into the implicit association task to examine how implicit biases were moderated in different behavioral contexts. The results demonstrated that the manifestation of implicit biases depended on the behavioral context as well as the distinctive identity created by the combinations of different social categories. Chapter 3 laid groundwork for testing working memory as the processing capacity for the bias regulation mechanism. The result suggested that the hand-motion tracking indices of working memory load could be used to infer the capacity of an individual to suppress the influence of implicit bias. In Chapter 4, the mouse-tracking paradigm was integrated into the Stroop task with implicit associations serving as the Stroop targets. The implicit associations produced various effects including the conflict adaptation effect, like the Stroop targets, which suggested that implicit associations and Stroop stimuli are handled by overlapping cognitive mechanisms. Throughout these efforts, the current dissertation, first, demonstrated that a more ecologically valid and multidimensional approach is required to understand biased behaviors in detail. Furthermore, the current dissertation suggested the cognitive control mechanism as a finer definition for the locus of the bias regulation mechanism, which could be leveraged to offer solutions that are more adaptive and effective in the environment where collaboration and harmony are more important than ever.
ContributorsRheem, Hansol (Author) / Becker, D. Vaughn (Thesis advisor) / Craig, Scotty D. (Committee member) / Gutzwiller, Robert S. (Committee member) / Arizona State University (Publisher)
Created2019
Description
It is difficult to imagine a society that does not utilize teams. At the same time, teams need to evolve to meet today’s challenges of the ever-increasing proliferation of data and complexity. It may be useful to add artificial intelligent (AI) agents to team up with humans. Then, as AI agents are integrated into the team, the first study asks what roles can AI agents take? The first study investigates this issue by asking whether an AI agent can take the role of a facilitator and in turn, improve planning outcomes by facilitating team processes. Results indicate that the human facilitator was significantly better than the AI facilitator at reducing cognitive biases such as groupthink, anchoring, and information pooling, as well as increasing decision quality and score. Additionally, participants viewed the AI facilitator negatively and ignored its inputs compared to the human facilitator. Yet, participants in the AI Facilitator condition performed significantly better than participants in the No Facilitator condition, illustrating that having an AI facilitator was better than having no facilitator at all. The second study explores whether artificial social intelligence (ASI) agents can take the role of advisors and subsequently improve team processes and mission outcome during a simulated search-and-rescue mission. The results of this study indicate that although ASI advisors can successfully advise teams, they also use a significantly greater number of taskwork interventions than teamwork interventions. Additionally, this study served to identify what the ASI advisors got right compared to the human advisor and vice versa. Implications and future directions are discussed.
ContributorsBuchanan, Verica (Author) / Cooke, Nancy J. (Thesis advisor) / Gutzwiller, Robert S. (Committee member) / Roscoe, Rod D. (Committee member) / Arizona State University (Publisher)
Created2023