A Life-Threatening Emergency Exacerbated by Untreated Mental Illness in a Low-Barrier Health Center

Introduction: We report on a patient with untreated severe mental illness who presented with a life-threatening emergency: retained products of conception and hemorrhage. Clinical Findings: A female patient experiencing homelessness developed life-threatening hemorrhage. Her mental illness impaired effective communication and treatment. Clinical Course: The patient presented with fatigue, vaginal bleeding, and known retained products of conception. Her active mental illness complicated the situation as it limited effective communication and treatment due to delusions. She requested only treatment for an infectious cause of her symptoms. She refused most interventions and had a self-directed discharge from the hospital. Throughout this process, we assessed that she understood the implications of declining care, despite her mental illness. After extensive patient-centered and trauma-informed discussions, she accepted medical treatment. Conclusions: This case highlights the importance of patient-centered communication and team-based care during emergencies and refusal of care. Shared decision-making and trauma-informed care are appropriate methods for assessing the capacity of patients with severe mental illness in acute and life-threatening conditions

Evidence Inference 2.0: More Data, Better Models

How do we most effectively treat a disease or condition? Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions. Unfortunately, no such database exists; clinical trial results are instead disseminated primarily via lengthy natural language articles. Perusing all such articles would be prohibitively time-consuming for healthcare practitioners; they instead tend to depend on manually compiled systematic reviews of medical literature to inform care. NLP may speed this process up, and eventually facilitate immediate consult of published evidence. The Evidence Inference dataset was recently released to facilitate research toward this end. This task entails inferring the comparative performance of two treatments, with respect to a given outcome, from a particular article (describing a clinical trial) and identifying supporting evidence. For instance: Does this article report that chemotherapy performed better than surgery for five-year survival rates of operable cancers? In this paper, we collect additional annotations to expand the Evidence Inference dataset by 25\%, provide stronger baseline models, systematically inspect the errors that these make, and probe dataset quality. We also release an abstract only (as opposed to full-texts) version of the task for rapid model prototyping. The updated corpus, documentation, and code for new baselines and evaluations are available at http://evidence-inference.ebm-nlp.com/.Comment: Accepted as workshop paper into BioNLP Updated results from SciBERT to Biomed RoBERT

Visualizing basic accounting flows : does XBRL + model + animation = understanding?

The usefulness of XBRL (eXtensible Business Reporting Language) in facilitating efficient data sharing is clear, but widespread use of XBRL also promises to support more effective analysis processes. This format should allow managers, investors, regulators, and students to aggregate, compare and analyze financial information. This study explores an XBRL-based visualization tool that maps the organization of financial statements captured in the XBRL formalism into a graphical representation that organizes, depicts, and animates financial data. We show that our tool integrates and presents profitability, liquidity, financing, and market value data in a manner recognizable to business students. Our findings suggest the promise of XBRL-based visualization tools both in helping students grasp basic accounting concepts and in facilitating financial analysis in general

A Normative Model for Assessing SME IT Effectiveness

Information technology (IT) is a key enabler of modern small businesses, yet fostering reliably effective IT systems remains a significant challenge. This paper presents a light weight IT effectiveness model for small businesses to assess their IT and formulate strategies for improvement. Employing an action research approach we investigate a mixed method analysis of 120 survey responses from small family businesses and user participation in 10 semi-structured interviews. We then conduct critical reflection to identify refinements which are validated using 72 survey responses from university students. The results present compelling evidence that employees’ normative patterns (norms) are a significant driver of IT effectiveness in a second order PLS predictive model able to explain 26% of observed variance. A norms-based approach to IT effectiveness helps fill a significant research and managerial gap for organizations unable or unwilling to adopt IT best practice frameworks used by large organizations. Our findings imply that comparing norms to IT best practices may offer a less technical approach to assessing IT operations, which may be well suited to small businesses. Although further investigation cycles are needed to systematically test this model, we encourage small business managers to: 1) anticipate IT risks and mitigate them; 2) identify measures of IT performance, and monitor them, and 3) review/synchronize business and IT goals

Integrated agricultural riparian stewardship in the Stillaguamish and Snohomish watersheds

The Stillaguamish and Snohomish River watersheds are regionally important to the health of Puget Sound and the Salish Sea and in particular for the recovery of salmon. The habitat gains needed to achieve salmon recovery in these watersheds include much of the agricultural landscape in Snohomish County, a situation that often results in conflicts between salmon recovery and agricultural communities. The Snohomish Conservation District’s National Estuary Program-funded Integrated Riparian Stewardship project is one of several efforts aimed at simultaneously achieving agricultural land preservation and salmon habitat protection and restoration in one of the fastest growing counties in the United States, where pressures from development and climate change threaten both salmon recovery and agriculture, and where agricultural property owners may feel salmon recovery efforts impact their livelihood and way of life. The District and project partners undertook a planning and protection/restoration initiative to identify, protect, and restore high priority riparian agricultural and rural lands in the Stillaguamish and Snohomish watersheds. The District developed an Action Plan that identifies high priority areas for reach-scale riparian protection and restoration and outlines a comprehensive riparian zone management approach for three focus areas in the Stillaguamish and Snohomish watersheds. The Action Plan integrates existing geomorphic, habitat, hydrologic, and water quality studies to identify reaches and parcels on which to purchase conservation easements and complete habitat restoration projects in order to protect or enhance cold water inflows, restore salmon habitat, and improve or protect hydrologic processes. The District is currently implementing the Action Plan. We will share our experience with a precision outreach strategy and our successes in leveraging the easement program with grant funding and the Conservation Reserve Enhancement Program and other grant funding to secure landowner cooperation and achieve reach-scale riparian zone management. Partners include Snohomish County, Washington Department of Ecology, Forterra, and NOAA Restoration Center

Do measures of security compliance intent equal non-compliance scenario agreement?

To better protect organizations from the threat of insiders, IS security (ISS) research frequently emphasizes IS Security Policy (ISP) behavior. The effectiveness of an assessment model is typically analyzed either using short survey statements (behavior survey) or by using scenario agreement (prospective scenario) to measure current and prospective compliance (or non-compliance) behavior. However, a significant gap is the lack of statistical evidence to demonstrate that these two measures or dependent variables (DV) sufficiently agree with one another. We report on an effort to compare and contrast two assessment models which employed alternate styles of DVs and demonstrate that the primary construct from two different ISS behavioral theories had approximately the same effect size on either of the DVs. Our findings add support for substantial (but not overly correlated) synchronization between the two DV values, since we also observe that the prospective scenario non-compliance measure resulted in lower model fit while the behavior survey compliance measures fit both models with higher accuracy. We discuss our findings and recommend that for many studies there can be value in employing both DVs

Will SOC telemetry data improve predictive models of user riskiness? A work in progress

Security Operation Centers (SOC) play a key role in protecting organizations from many cybersecurity threats, such as system intrusion or information breaches. A major challenge in improving SOC operations is the adequacy of the data used to identify such threats. Detection tools employed by SOCs are largely based on observable telemetry indicators (e.g., network traffic patterns or system logs and activities collected from user devices). However, the use of such telemetry data without understanding human behaviors in-depth can lead to increasing false-positive alerts. Prior work shows that it can even be a more significant problem when analysts largely ignore alerts if they are overwhelmingly false-positive. These false positive alerts raise SOC analysts’ cognitive workload, diminish conscious cognitive processing, and decrease their trust in future alerts

FEAR APPEALS VERSUS PRIMING IN RANSOMWARE TRAINING

Employee non-compliance is at the heart of many of today’s security incidents. Training programs often employ fear appeals to motivate individuals to follow policy and take action to reduce security risks. While the literature shows that fear appeals drive intent to comply, there is much less evidence of their impact after intention is formed. Building on IPAM – a process nuanced model for compliance training and assessment – this study contrasts the impact of fear appeals vs. self-efficacy priming on ransomware training. In our proposed study, a pool of students will participate in a three-step series of training events. Some participants will encounter enhanced fear appeals at each step while others will be presented with materials that include priming signals intended to foster development of increased self-efficacy. Previously identified drivers of behavior (intent, processed-nuanced forms of self-efficacy, and outcome expectations) are measured so that the effect of the treatments can be contrasted. A scenario agreement methodology is used to indicate behavior as a dependent variable. We expect to show that while fear appeals are useful and help build intent to comply at the motivational stage, process-nuanced self-efficacy treatments are expected have a stronger effect on behavior post-intentional

Spá:A Web-Based Viewer for Text Mining in Evidence Based Medicine

Summarizing the evidence about medical interventions is an immense undertaking, in part because unstructured Portable Document Format (PDF) documents remain the main vehicle for disseminating sci- entific findings. Clinicians and researchers must therefore manually ex- tract and synthesise information from these PDFs. We introduce Spá,12 a web-based viewer that enables automated annotation and summari- sation of PDFs via machine learning. To illustrate its functionality, we use Spá to semi-automate the assessment of bias in clinical trials. Spá has a modular architecture, therefore the tool may be widely useful in other domains with a PDF-based literature, including law, physics, and biology

Personal Motivation Measures for Personal IT Security Behavior

While IT security research has explored explanatory models using risk/fear/efficacy drivers, this effort emphasizes assessments of personal security optimism/pessimism as drivers of personal security behavior. Technical solutions can help but many organizational vulnerabilities are exacerbated by non-compliance. Individuals neglect to or choose not to comply with security practices, placing organizations at risk. In this study, we explore a model that identifies likely non-compliers. We assess constructs over time, assess perceptions of the pros and cons of compliance, and deliver small training/motivational content. In our results measuring over time and including pro/con perception increased explanatory power for compliance behavior and prediction algorithms were able to identify non-compliers with a high degree of accuracy. We assert that this approach, which integrates training and assessment over time and uses measures that may be more palatable for real-world settings, is promising for organizations who seek to both understand and improve security behavior