Fostering Transformative Learning, Self-reflexivity and Medical Citizenship Through Guided Tours of Disadvantaged Neighborhoods

Background and objectives: Medical school curricula increasingly seek to promote medical students’ commitment to redressing health disparities, but traditional pedagogical approaches have fallen short of this goal. The objective of this work was to assess the value of using community-based guided tours of disadvantaged neighborhoods to fill this gap. Methods: A total of 50 second-year medical students participated in a guided tour of disadvantaged public housing neighborhoods in Richmond, Virginia. Students completed self-reflexive writing exercises during a post-tour debriefing session. Student writings were analyzed to assess the tour’s effect on their awareness of poverty’s impact on vulnerable populations’ health and wellbeing, and their personal reactions to the tour. Results: Student writings indicated that the activity fostered transformative learning experiences around the issue of poverty and its effects on health and stimulated a personal commitment to working with underserved populations. Themes from qualitative analysis included: increased awareness of the extent of poverty, enhanced self-reflexive attitude towards personal feelings, biases and misperceptions concerning the poor, increased intentional awareness of the effects of poverty on patient health and well-being, and, encouragement to pursue careers of medical service. Conclusions: This pilot demonstrated that incorporating self-reflexive learning exercises into a brief community-based guided tour can enhance the social consciousness of medical students by deepening understandings of health disparities and promoting transformative learning experiences

Cross Hedging with Single Stock Futures

This study evaluates the efficiency of cross hedging with the new single stock futures (SSF) contracts recently introduced in the United States. We use matched sample estimation techniques to select SSF contracts that will reduce the basis risk of crossing hedging and will yield the most efficient hedging portfolio. Employing multivariate matching techniques with cross-sectional matching characteristics, we can improve hedging efficiency while at the same time overcoming the contingency of the correlation between spot and futures prices on the sample period and length. Overall, we find that the best hedging performance is achieved through a portfolio that is hedged with market index futures and a SSF matched by both historical return correlation and cross-sectional matching characteristics. We also find it preferable to retain the chosen SSF contracts for the whole out-of-sample period but to re-estimate the optimal hedge ratio for each rolling window.

Technofixing the Future: Ethical Side Effects of Using AI and Big Data to meet the SDGs

While the use of smart information systems (the combination of AI and Big Data) offer great potential for meeting many of the UN’s Sustainable Development Goals (SDGs), they also raise a number of ethical challenges in their implementation. Through the use of six empirical case studies, this paper will examine potential ethical issues relating to use of SIS to meet the challenges in six of the SDGs (2, 3, 7, 8, 11, and 12). The paper will show that often a simple “technofix”, such as through the use of SIS, is not sufficient and may exacerbate, or create new, issues for the development community using SIS

Zoology One Efficacy Evaluation Summary of Findings (April 2020)

Helping young children become proficient readers is a critical goal. Research tells us that students who experience difficulty reading in the early years of school often struggle to catch up (Stanley, Petscher, & Catts, 2018; Ozernov, , Palchik et al., 2016; Cunningham & Stanovich, 1997; Francis, Shaywitz, Stuebing, Shaywitz, & Fletcher, 1996). This study1 focuses on an innovative curriculum for kindergarten that closely integrates literacy instruction and science exposure. The research study combines a rigorous randomized controlled trial with in-depth cost and implementation studies to investigate impacts

Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme

The estimation of changes in free energy upon mutation is central to the problem of protein design. Modern protein design methods have had remarkable success over a wide range of design targets, but are reaching their limits in ligand binding and enzyme design due to insufficient accuracy in mutational free energies. Alchemical free energy calculations have the potential to supplement modern design methods through more accurate molecular dynamics based prediction of free energy changes, but suffer from high computational cost. Multisite λ dynamics (MSλD) is a particularly efficient and scalable free energy method with potential to explore combinatorially large sequence spaces inaccessible with other free energy methods. This work aims to quantify the accuracy of MSλD and demonstrate its scalability. We apply MSλD to the classic problem of calculating folding free energies in T4 lysozyme, a system with a wealth of experimental measurements. Single site mutants considering 32 mutations show remarkable agreement with experiment with a Pearson correlation of 0.914 and mean unsigned error of 1.19 kcal/mol. Multisite mutants in systems with up to five concurrent mutations spanning 240 different sequences show comparable agreement with experiment. These results demonstrate the promise of MSλD in exploring large sequence spaces for protein design.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146479/1/pro3500_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146479/2/pro3500.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146479/3/pro3500-sup-0001-appendixS1.pd