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The literature provides multiple measures of diversity along a single demographic dimension, but when it comes to studying the interaction of multiple diversity types (e.g. age, gender, and race), the field of useable measures diminishes. We present the use of decision trees as a machine learning technique to automatically identify the interactions across diversity types to predict different levels of a dependent variable. In order to demonstrate the power of decision trees, we use five types of surface-level diversity (age, gender, education level, religion, and region of origin) measured via the standardized Blau index as independent variables and knowledge sharing as the dependent variable. The results of our decision tree approach relative to linear regression show that decision trees serve as a powerful tool to identify key demographic faultlines without "a priori" specification of a model structure.

While the outcome of the COVID-19 pandemic was largely the same across higher education institutions (HEI) – a rapid transition to online teaching and learning (OTL) – the processes and institutional support leading to this transition varied greatly. As such, the perception and anxieties experienced by faculty warrant exploration as these perceptions likely dictate the future of online teaching and learning within higher education institutions. Through the use of tweets made during the pandemic and interviews with faculty, this study reveals the emotional stress experienced by faculty when playing multiple, unfamiliar roles hindered the implementation of online teaching and learning initiatives. This hindrance may in part be alleviated through well-targeted institutional support. [ABSTRACT FROM AUTHOR]

Emergency Medical Services (EMS) are essential to the medical healthcare landscape. EMS play a crucial role in maximizing the overall expected survival probability of patients with various health emergencies. During the COVID-19 pandemic, peoples’ lifestyle changed and their decisions to seek medical assistance were mixed with fear. This affected the type and number of missions that EMS responded to worldwide. Furthermore, COVID-19 affected EMS systems in terms of response protocols and personal protective equipment levels. These changes influenced the EMS’ provision of these essential services – fluctuations in demand impacted capacity decisions, the need for PPE altered response times, and lockdowns influenced roadway dynamics. This research focuses on describing and analyzing the performance of a major EMS provider in Lebanon in terms of response time to emergency missions during the COVID-19 pandemic compared to previous years and to the context of EMS in other countries. Results show that the number of calls and number of missions dropped yet, the emergency response time was found to be higher than previous years. The change in response time is most strongly tied to capacity allocation and PPE protocols. This study is an initial step toward exploring EMS system dynamics, with the goal of effectively allocating resources for improved EMS service during periods of heightened uncertainty and variability.

The literature provides multiple measures of diversity along a single demographic dimension, but when it comes to studying the interaction of multiple diversity types (e.g. age, gender, and race), the field of useable measures diminishes. We present the use of decision trees as a machine learning technique to automatically identify the interactions across diversity types to predict different levels of a dependent variable. In order to demonstrate the power of decision trees, we use five types of surface-level diversity (age, gender, education level, religion, and region of origin) measured via the standardized Blau index as independent variables and knowledge sharing as the dependent variable. The results of our decision tree approach relative to linear regression show that decision trees serve as a powerful tool to identify key demographic faultlines without a priori specification of a model structure. [ABSTRACT FROM AUTHOR]

Imagine working as a dispatcher for a medium-sized freight logistics company. Your day begins by matching a set of orders to a group of drivers. Maybe a computer helps you […]