Cognitive modeling for understanding interactions between people and decision support tools in complex and uncertain environments: A study protocol.

Background: Recent advances in Computational Intelligence Tools and the escalating need for decision-making in the face of complex and uncertain phenomena like pandemics, climate change, and geopolitics necessitate understanding the interaction between these tools and human behavior. It is crucial to efficiently utilize the decision-makers cognitive resources in addressing specific problems.

Methods: The main goal of this present protocol is to describe the effect that CITs (Computational Intelligence Tools) have on decisions made during complex and uncertain situations.

Current research trends on cognition, integrative complexity, and decision-making: a systematic literature review using activity theory and neuroscience.

Introduction: This article presents a systematic literature review that follows the PRISMA and PICOS guidelines to analyze current research trends on cognition, integrative complexity (IC) (a cognitive feature focusing on information processing in a person's response rather than its quantity or quality), and decision-making from the perspectives of activity theory and neuroscience.

Methods: The study examines 31 papers published between 2012 and 2022 and 19 articles specifically related to neuroscience. We performed a content analysis using six categories within activity theory: subjects, objects, rules, community, division of labor, and outcomes.

Predictive Analytics for Retention in Care in an Urban HIV Clinic.

Consistent medical care among people living with HIV is essential for both individual and public health. HIV-positive individuals who are 'retained in care' are more likely to be prescribed antiretroviral medication and achieve HIV viral suppression, effectively eliminating the risk of transmitting HIV to others. However, in the United States, less than half of HIV-positive individuals are retained in care.

An Experience-Centered Approach to Training Effective Data Scientists.

Like medicine, psychology, or education, data science is fundamentally an applied discipline, with most students who receive advanced degrees in the field going on to work on practical problems. Unlike these disciplines, however, data science education remains heavily focused on theory and methods, and practical coursework typically revolves around cleaned or simplified data sets that have little analog in professional applications. We believe that the environment in which new data scientists are trained should more accurately reflect that in which they will eventually practice, and we propose here a data science master's degree program that takes inspiration from the residency model used in medicine.