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Letter of Concern from the Association of Academic Chairs of Emergency Medicine Regarding ACGME Proposed Changes
No full textThis letter, signed by over 50 academic chairs of emergency medicine, urges the ACGME to reconsider a proposed mandate requiring all emergency medicine residency programs to adopt a four-year training model. The authors argue that current three-year programs are supported by data demonstrating equivalent educational and clinical outcomes compared to four-year formats. They criticize the flawed survey methodology underpinning the proposal, note the loss of milestone-based training flexibility, and highlight the lack of added scholarly or clinical value in the fourth year. The letter also outlines negative consequences for fellowship participation, workforce development, trainee debt, and diversity. The signatories advocate for maintaining the current flexible training model to preserve excellence, equity, and innovation in emergency medicine education
Leveraging Large Language Models to Create Learner Personas for Training Design
No full textThis case examines the innovative use of Large Language Models (LLMs) to generate learner personas for developing learner-centered cybersecurity training materials when direct access to initial learner data is not available. The team developed a nine-stage iterative process for creating and refining AI-generated personas to address this constraint, integrating ethical review, stakeholder feedback, and action research principles. The process expanded upon Kouprie and Visser’s (2009) empathic design framework to ensure cultural responsiveness and mitigate potential biases in LLM outputs. Through multiple refinement cycles, initial generic personas evolved into detailed, context-rich archetypes which informed the development of effective and context-responsive training materials. The case demonstrates how AI-generated personas can serve as valuable tools for instructional design in emerging technical domains when developed through rigorous iterative processes and ethical oversight. This approach offers insights for educational initiatives facing similar challenges in understanding diverse learner needs before direct audience data becomes available
“We Are Trans, But We’re Not Transitional”: Exploring Safety, Place, and Space in the Lives of Transgender Individuals in Southeastern Virginia
No full textIn general, little research exists on trans people’s spatial experiences despite space being highly relevant to their experiences of violence and perceptions of safety. To date, two criminological studies have examined LGBTQ people’s fear of crime in relation to public or private places and spaces. This is in stark contrast to the sizable place and space literature on cisgender men and women. These topics from the perspectives of trans people are underexplored primarily because most criminological research has historically upheld binarized understandings of gender. Safety should not be privileged or limited to certain groups, individuals, or spaces. Instead, safety should be accessible and experienced by everyone, regardless of their background or identity. Spaces facilitate and are influenced by the interactions and relationships within them. It is thus essential that the spatial experiences of trans people are also accounted for in criminological research. Similarly, safety has been historically conceptualized through a hetero– cisnormative lens. In doing so, this excludes how safety is perceived, experienced, negotiated, and defined by trans people.
This dissertation explores how gender and cultural repertoires shape the spatial experiences of 30 trans people residing in Southeastern Virginia based on their perceptions of safety. Specifically, I was interested in how they move through place and space in a hetero–cis– normative society—a society that often devalues those of non–normative identities and which subjects them to violence. Using in–depth semi–structured interviews, the current study explores four broad questions: 1) What is safety to trans people and how is it determined? 2) What social, cultural, and institutional challenges do trans people face when moving through place and space, 3) How do trans people’s perceptions of safety shape their spatial mobility and interactions in public, private, and semi–private spaces, and 4) What protective strategies do trans people use to navigate and negotiate their environments amidst systemic and interpersonal challenges? Investigating these questions through both queer and cultural criminological perspectives, this research provides insight into how trans people conceptualize safety, the social, cultural, and institutional factors that limit their spatial mobility, what can be done to increase their public safety, and the protective strategies they engage in to mitigate potential transphobic risks in public, private, and semi–private spaces.
The findings show that for these trans folks, safety is an intersectional, multidimensional concept encompassing physical, social, emotional, and intersectional well–being influenced by systemic issues of transphobia and cisnormativity. Moreover, the findings point to how social dynamics and verbal/non–verbal cues (e.g., “the vibe” of a place) play a pivotal role in shaping perceptions of safety, especially by those occupying positions of social and institutional authority such as the police and other authority figures. Notably, the findings highlight how structural power dynamics and historical abuses shape perceptions of unsafety. Lastly, cultural repertoires function as tools for both survival and resistance, enriching the empirical understanding of how marginalized groups navigate potentially hostile environments. By documenting trans people’s protective strategies, this research highlights how they exert agency within these constraints— trans people are more than mere subjects to systems of oppression, they are active agents shaping their own spatial and social experiences
Fares on Fairness: Using a Total Error Framework to Examine the Role of Measurement and Representation in Training Data on Model Fairness and Bias
No full textData-driven decisions, often based on predictions from machine learning (ML) models are becoming ubiquitous. For these decisions to be just, the underlying ML models must be fair, i.e., work equally well for all parts of the population such as groups defined by gender or age. What are the logical next steps if, however, a trained model is accurate but not fair? How can we guide the whole data pipeline such that we avoid training unfair models based on inadequate data, recognizing possible sources of unfairness early on? How can the concepts of data-based sources of unfairness that exist in the fair ML literature be organized, perhaps in a way to gain new insight? In this paper, we explore two total error frameworks from the social sciences, Total Survey Error and its generalization Total Data Quality, to help elucidate issues related to fairness and trace its antecedents. The goal of this thought piece is to acquaint the fair ML community with these two frameworks, discussing errors of measurement and errors of representation through their organized structure. We illustrate how they may be useful, both practically and conceptually
Diverticulitis—New Evidence to Share With Patients
No full textDiverticulitis is one of the most common gastrointestinal causes of hospitalization in Western society. While previously characterized as a disease of older patients, new literature highlights an increasing incidence among the younger population. Over the past few decades, the understanding of etiology and management of diverticulitis has changed drastically. New data refute past beliefs while promoting other novel recommendations to mitigate incidence and subsequent complications. Data now confirms the safety and possible protective benefit of particulate food, while highlighting evidence-based approaches for the use of diagnostic imaging and antibiotics. We recognize modifiable and non-modifiable risk factors that are commonly seen throughout the literature and play a significant role in the management and prevention of diverticulitis. Emerging evidence also links chronic inflammation with subsequent microbial dysbiosis and alterations in the neuroendocrine system, leading to visceral hypersensitivity and perturbation of the gut-brain axis. This review provides a comprehensive update on acute uncomplicated diverticulitis according to the most recent evidence-based literature, encompassing the risks, diagnostic modalities, and management treatment regimens
TWiM #222: Biosensors in Bacteria
No full textPodcast annotation TWiM #222: Biosensors in Bacteria from the weekly podcast series This Week in Microbiology (TWiM), a podcast where experts in microbiology discuss academic papers in their field in an informal way
SCITEUQ: Toward Uncertainty-Aware Complex Scientific Table Data Extraction and Understanding
No full textScientific tables report critical research insights, data, and findings for scientific progress. Because Portable Document Format (PDF) is the de facto standard format for scientific paper publishing, there has been an emerging need for an automatic method to extract data from PDF files. A significant fraction of scientific tables exhibit complex structure and content, making it challenging for machine learning tools to accurately extract the content directly from PDF files. Despite the advancements in Table Structure Recognition (TSR), automated extraction of data from complex scientific tables remains a challenge due to variations in table structures and contents. In this dissertation, we developed SCITEUQ, a software framework to address these challenges by enabling automated, accurate, and uncertainty-aware extraction of data from complex scientific tables in multiple disciplines. By integrating TSR with Optical Character Recognition and Uncertainty Quantification, SCITEUQ aims at significantly improving the quality of data extraction while significantly reducing the workload of humans to verify extracted data. We also developed SciTableQA, a benchmark for evaluating the question-answer and reasoning capabilities of Large Language Models on complex scientific tables. This research advances the fields of information extraction for complex scientific tables, which will potentially benefit scientific data compilation in a wide range of scientific domains
The Influence of Individual and Collective Social Capital on Teachers’ Decisions to Move Districts
No full textUnderstanding what contributes to teachers’ decisions to stay or leave in a school district is complex. There is a need to further investigate factors that influence teacher retention and attrition so that policy makers and school leaders can be informed about practices that may replenish and sustain the educator workforce. In an effort to understand the essence of the lived experience of teachers who previously made a decision to move districts, this study used phenomenological methods for the purpose of discovering the role that access to individual and collective social capital played in teachers’ decision making. Semi-structured interviews were conducted with 8 public school teachers who had moved to a rural division in southeast Virginia. Narrative data were analyzed using an iterative process of engagement to illuminate the underlying meanings of participants\u27 experiences. Initial broad units of meaning were identified. The next phase of analysis included a deductive analysis that drew on Social Capital Theory as a guiding lens. Meaning units were then clustered and synthesized into experiential patterns that revealed shared and divergent aspects of the phenomenon. These thematic clusters illuminated the relational and organizational factors influencing teachers’ decisions to move between districts, thereby allowing the narrative data to be reduced to a composite summary. Inductive discovery paired with a deductive analysis using Social Capital Theory as a lens elucidated how social relationships contributed to decision making when determining when to stay or move.
Teachers were rooted, or uprooted, by the relationships in their school environment. Teachers’ decisions to move were often driven by the quality of human connection within their schools. Findings indicate a need to prioritize relational leadership and structures that increase access to social capital
Acute Exacerbations of Chronic Rhinosinusitis
No full textPurpose of Review
We aim to highlight recent advancements on the evolving chronic rhinosinusitis (CRS) phenotype: acute exacerbations of chronic rhinosinusitis (AECRS). We focused on studies that expanded the current understanding of its pathophysiology, patient characteristics, and disease burden.
Recent findings
Defining AECRS has been a topic of discussion for many years. A recent regulatory definition of AECRS in the literature incorporates a \u3e 3 day requirement of worsened symptoms and an escalation of treatment. It is important not to rely on patient-reported rescue medication frequency as it was recently demonstrated these are only obtained for 1/3 of reported AECRS episodes. The pathophysiology behind AECRS is still being evaluated but it appears irritants such as viral insult to the sinonasal microbiome can create a dysbiosis and worsens host immune system breakdown, facilitating a subsequent bacterial infection.
Summary
Many studies are using loose definitions of AECRS because no formal definition has existed until recently. Clinical trials and other studies are relying on patient-reported illnesses, CRS-related antibiotics, and CRS-related corticosteroids to determine an episode of AECRS. Formally defining AECRS is vital in order to conduct future literature on its etiology and clinical outcomes so results may be translatable. Additionally, our review demonstrates that CRS patients with asthma and/or concomitant allergic rhinitis appear to be at an increased risk for developing AECRS and future research should continue to investigate their interplay. Many patients are being overprescribed antibiotics and corticosteroids for reported AECRS episodes. This increases total healthcare spending and increases the risk for adverse effects from corticosteroids and antibiotic resistance. Future research should investigate methods to mitigate this practice
Enhancing Risk and Crisis Communication with Computational Methods: A Systematic Literature Review
Get PDFRecent developments in risk and crisis communication (RCC) research combine social science theory and data science tools to construct effective risk messages efficiently. However, current systematic literature reviews (SLRs) on RCC primarily focus on computationally assessing message efficacy as opposed to message efficiency. We conduct an SLR to highlight any current computational methods that improve message construction efficacy and efficiency. We found that most RCC research focuses on using theoretical frameworks and computational methods to analyze or classify message elements that improve efficacy. For improving message efficiency, computational and manual methods are only used in message classification. Specifying the computational methods used in message construction is sparse. We recommend that future RCC research apply computational methods toward improving efficacy and efficiency in message construction. By improving message construction efficacy and efficiency, RCC messaging would quickly warn and better inform affected communities impacted by current hazards. Such messaging has the potential to save as many lives as possible