Hospital to Home: Fall Prevention Interventions for the Discharging Patient

Falls is a major public health problem globally, with an estimated 646,000 fatal falls per year. This makes falls the second leading cause of unintentional injury death. Falls are very costly with non-fatal fall injuries costing about $50 billion per year and fatal falls with an estimated$754 million. Many risk factors contribute to a person’s risk of falling. Risk factors include age, gender, muscle strength, underlying medical or disabling conditions, and unsafe environments. Patients who have been hospitalized are also among those at risk. Most hospitalized patients are assessed frequently to determine their risk of falling so that care plans can be adjusted to implement strategies to avoid a fall. However, nurses frequently discharge patients with little to no education or tools to prevent falls at home. The purpose of this scholarly inquiry project is to explore the best practices for fall prevention after discharging home from the hospital. An extensive integrative literature review highlighted evidence that supports and arms patients, families, and support systems with tools that will help prevent a fall at home after being discharged from the hospital. A conceptual map details the interventions that need to be integrated at discharge to help create a home fall prevention plan of care. Three themes emerged from the literature and include criteria for implementing falls risk discharge interventions, fall risk discharge interventions, and the outcomes from the interventions. Recommendations for nursing are also built into this project that can guide nurses in protecting patients by implementing evidence-based strategies to prevent patients from falling at home after discharge and decrease the risk of reoccurring hospitalizations or fall fatality

Taking the Intentional Stance Seriously, or "Intending" to Improve Cognitive Systems

Finding claims that researchers have made considerable progress in artificial intelligence over the last several decades is easy. However, our everyday interactions with cognitive systems (e.g., Siri, Alexa, DALL-E) quickly move from intriguing to frustrating. One cause of those frustrations rests in a mismatch between the expectations we have due to our inherent, folk-psychological theories and the real limitations we experience with existing computer programs. The software does not understand that people have goals, beliefs about how to achieve those goals, and intentions to act accordingly. One way to align cognitive systems with our expectations is to imbue them with mental states that mirror those we use to predict and explain human behavior. This paper discusses these concerns and illustrates the challenge of following this route by analyzing the mental state 'intention.' That analysis is joined with high-level methodological suggestions that support progress in this endeavor.Comment: 13 pages, 1 figure, 2 table

Is Organic Food More Nutritious?

Organically grown food has the notion that it is healthier than its conventionally grown counterparts. There is substantial research that has been done on the effects of pesticides on produce and how these foreign chemicals have negative effects on the environment and the people consuming these chemicals. While this is a known fact, the big question is if organic food is more nutritious than its conventional counter parts. There is minimal research on the actual nutritional quality of organically grown produce versus conventionally grown produce. Various studies have looked at vitamins, minerals and antioxidants. There are many discrepancies among the studies looking at nutritional quality, as they did not all measure the same nutrients or use the same variables. The produce examined included the following: pistachios, chickpeas, oranges, strawberries, potatoes, tomatoes, cucumbers and onions. There were few studies that had overlaps among the nutrients that were measured. Even among the discrepancies, some conclusions can be drawn with regards to nutritional quality but a better evaluation of choosing to “go organic” should be based on a multitude of factors including time of life, the environmental concerns and income considerations. More research on nutritional quality would need to be completed for a more accurate conclusion to be drawn with regards to which type of farming produces the most nutritional fruits and vegetables. At this point in time, the decision to eat organic produce should be based on the pesticide usage

Black Lives Matter: Why Black Feminism?

In this essay, the author explores the inclusive nature and focal range of the Black Lives Matter movement in an effort to demonstrate how the goals of the movement are grounded in Black feminism. Ultimately, Bridewell concludes that creating inclusive spaces for the exploration of intersectional identities can help bring justice and equality not only to the Black community, but to all lives that have be oppressed or marginalized

Science as an Anomaly-Driven Enterprise: A Computational Approach to Generating Acceptable Theory Revisions in the Face of Anomalous Data

Anomalous data lead to scientific discoveries. Although machine learning systems can be forced to resolve anomalous data, these systems use general learning algorithms to do so. To determine whether anomaly-driven approaches to discovery produce more accurate models than the standard approaches, we built a program called Kalpana. We also used Kalpana to explore means for identifying those anomaly resolutions that are acceptable to domain experts. Our experiments indicated that anomaly-driven approaches can lead to a richer set of model revisions than standard methods. Additionally we identified semantic and syntactic measures that are significantly correlated with the acceptability of model revisions. These results suggest that by interpreting data within the context of a model and by interpreting model revisions within the context of domain knowledge, discovery systems can more readily suggest accurate and acceptable anomaly resolutions

Apophatic science:How computational modeling can explain consciousness

This study introduces a novel methodology for consciousness science. Consciousness as we understand it pretheoretically is inherently subjective, yet the data available to science are irreducibly intersubjective. This poses a unique challenge for attempts to investigate consciousness empirically. We meet this challenge by combining two insights. First, we emphasize the role that computational models play in integrating results relevant to consciousness from across the cognitive sciences. This move echoes Alan Newell’s call that the language and concepts of computer science serve as a lingua franca for integrative cognitive science. Second, our central contribution is a new method for validating computational models that treats them as providing negative data on consciousness: data about what consciousness is not. This method is designed to support a quantitative science of consciousness while avoiding metaphysical commitments. We discuss how this methodology applies to current and future research and address questions that others have raised

There Is No Agency Without Attention

For decades AI researchers have built agents that are capable of carrying out tasks that require human-level or human-like intelligence. During this time, questions of how these programs compared in kind to humans have surfaced and led to beneficial interdisciplinary discussions, but conceptual progress has been slower than technological progress. Within the past decade, the term agency has taken on new import as intelligent agents have become a noticeable part of our everyday lives. Research on autonomous vehicles and personal assistants has expanded into private industry with new and increasingly capable products surfacing as a matter of routine. This wider use of AI technologies has raised questions about legal and moral agency at the highest levels of government (National Science and Technology Council 2016) and drawn the interest of other academic disciplines and the general public. Within this context, the notion of an intelligent agent in AI is too coarse and in need of refinement. We suggest that the space of AI agents can be subdivided into classes, where each class is defined by an associated degree of control

Medium Altitude Long Endurance Remotely Piloted Aircraft Training: a Pilot Study in Blended Learning

Since April of 2011, research and development efforts between the Air Force Research Laboratory (AFRL) and the University of North Dakota (UND) have progressed through the “Science and Technology for Warfighter Training and Aiding.” Cooperative Agreement. One product of these cooperative efforts has been a Heads Down Display (HDD) Menu Trainer. Designed to familiarize students with the layout and manipulation of the HDD menus for either the MQ-1 or MQ-9, a parallel pretest/posttest design was designed to examine the efficacy of this HDD menu trainer as training aid in traditional, blended, and distance pedagogies. Results of a mixed ANOVA indicated the trainer significantly improved performance from pretest to posttest scores across all groups (pp