Children's Perceptions of Rainforest Biodiversity: Which Animals Have the Lion's Share of Environmental Awareness?

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The self-fulfilling prophecy in intensive care

Predictions of poor prognosis for critically ill patients may become self-fulfilling if life-sustaining treatment or resuscitation is subsequently withheld on the basis of that prediction. This paper outlines the epistemic and normative problems raised by self-fulfilling prophecies (SFPs) in intensive care. Where predictions affect outcome, it can be extremely difficult to ascertain the mortality rate for patients if all treatment were provided. SFPs may lead to an increase in mortality for cohorts of patients predicted to have poor prognosis, they may lead doctors to feel causally responsible for the deaths of their patients, and they may compromise honest communication with patients and families about prognosis. However, I argue that the self-fulfilling prophecy is inevitable when life-sustaining treatment is withheld or withdrawn in the face of uncertainty. SFPs do not necessarily make treatment limitation decisions problematic. To minimize the effects of SFPs, it is essential to carefully collect and appraise evidence about prognosis. Doctors need to be honest with themselves and with patients and their families about uncertainty and the limits of knowledge.Dominic Wilkinso

Simulation Modeling of Lakes in Undergraduate and Graduate Classrooms Increases Comprehension of Climate Change Concepts and Experience with Computational Tools

Ecosystem modeling is a critically important tool for environmental scientists, yet is rarely taught in undergraduate and graduate classrooms. To address this gap, we developed a teaching module that exposes students to a suite of modeling skills and tools (including computer programming, numerical simulation modeling, and distributed computing) that students apply to study how lakes around the globe are experiencing the effects of climate change. In the module, students develop hypotheses about the effects of different climate scenarios on lakes and then test their hypotheses using hundreds of model simulations. We taught the module in a 4-hour workshop and found that participation in the module significantly increased both undergraduate and graduate students' understanding about climate change effects on lakes. Moreover, participation in the module also significantly increased students' perceived experience level in using different software, technologies, and modeling tools. By embedding modeling in an environmental science context, non-computer science students were able to successfully use and master technologies that they had previously never been exposed to. Overall, our findings suggest that modeling is a powerful tool for catalyzing student learning on the effects of climate change.CeMaST (Center for Mathematics, Science, and Technology) at Illinois State University; National Science Foundation [DEB 1245707, ACI 1234983]We thank the amazing undergraduate students at Virginia Tech and graduate students in GLEON who participated in the Lake Modeling module and provided data for this study. We are grateful to the entire Project EDDIE team, especially Catherine O'Reilly, for their support and assistance. Saumitra Aditya, Ken Subratie, Renato Figueiredo, and Paul Hanson developed the distributed computing overlay network tools for the module as part of the GRAPLE (GLEON Research and Lake PRAGMA Expedition) team, and Jon Doubek and Kate Hamre provided invaluable assistance teaching the module. We appreciate administrative support provided by CeMaST (Center for Mathematics, Science, and Technology) at Illinois State University. This work was financially supported by grants from the National Science Foundation (DEB 1245707 and ACI 1234983)