Comedy Tonight!

BYU Guest Faculty Artists perform several enthralling Art Songs, meant to be different than a typical song and capture attention. All three performers are successful individual performers that now teach at BYU.https://digitalcommons.usu.edu/music_programs/1125/thumbnail.jp

Uber

Uber focuses primarily on the ride-hailing industry, which puts the company in direct competition with regular taxis. The company is like a lot of tech-driven, fast growing entrepreneurial firms in that it still struggles for profitability. Also, the popularity of this new form of transportation has put the company and its close competitors, such as Lyft, in the spotlight of government lawmakers and regulators. If they classify Uber drivers as employees rather than independent contractors, it could dramatically alter the Uber business model. This case is written in the aftermath of the ouster of one of the company’s co-founders as CEO, a not-so-successful initial public offering (IPO), and some very serious human resources issues associated with widely publicized instances of sexual harassment and mistreatment of drivers

American Piano Quartet

A performance by the American Piano Quartet.https://digitalcommons.usu.edu/music_programs/1217/thumbnail.jp

Data collection challenges in community settings: Insights from two field studies of patients with chronic disease

Purpose Collecting information about health and disease directly from patients can be fruitfully accomplished using contextual approaches, ones that combine more and less structured methods in home and community settings. This paper's purpose is to describe and illustrate a framework of the challenges of contextual data collection. Methods A framework is presented based on prior work in community-based participatory research and organizational science, comprised of ten types of challenges across four broader categories. Illustrations of challenges and suggestions for addressing them are drawn from two mixed-method, contextual studies of patients with chronic disease in two regions of the US. Results The first major category of challenges was concerned with the researcher-participant partnership, for example, the initial lack of mutual trust and understanding between researchers, patients, and family members. The second category concerned patient characteristics such as cognitive limitations and a busy personal schedule that created barriers to successful data collection. The third concerned research logistics and procedures such as recruitment, travel distances, and compensation. The fourth concerned scientific quality and interpretation, including issues of validity, reliability, and combining data from multiple sources. The two illustrative studies faced both common and diverse research challenges and used many different strategies to address them. Conclusion Collecting less structured data from patients and others in the community is potentially very productive but requires the anticipation, avoidance, or negotiation of various challenges. Future work is necessary to better understand these challenges across different methods and settings, as well as to test and identify strategies to address them

Simplifying External Load Data in NCAA Division-I Men\u27s Basketball Competitions: A Principal Component Analysis

The primary purpose was to simplify external load data obtained during Division-I (DI) basketball competitions via principal component analysis (PCA). A secondary purpose was to determine if the PCA results were sensitive to load demands of different positional groups (POS). Data comprised 229 observations obtained from 10 men\u27s basketball athletes participating in NCAA DI competitions. Each athlete donned an inertial measurement unit that was affixed to the same location on their shorts prior to competition. The PCA revealed two factors that possessed eigenvalues \u3e1.0 and explained 81.42% of the total variance. The first factor comprised total decelerations (totDEC, 0.94), average speed (avgSPD, 0.90), total accelerations (totACC, 0.85), total mechanical load (totMECH, 0.84), and total jump load (totJUMP, 0.78). Maximum speed (maxSPD, 0.94) was the lone contributor to the second factor. Based on the PCA, external load variables were included in a multinomial logistic regression that predicted POS (Overall model, p \u3c 0.0001; AUCcenters = 0.93, AUCguards = 0.88, AUCforwards = 0.80), but only maxSPD, totDEC, totJUMP, and totMECH were significant contributors to the model\u27s success (p \u3c 0.0001 for each). Even with the high significance, the model still had some issues differentiating between guards and forwards, as in-game demands often overlap between the two positions. Nevertheless, the PCA was effective at simplifying a large external load dataset collected on NCAA DI men\u27s basketball athletes. These data revealed that maxSPD, totDEC, totJUMP, and totMECH were the most sensitive to positional differences during competitions. To best characterize competition demands, such variables may be used to individualize training and recovery regimens most effectively