Central Penn College

Abstract Emergency department (ED) overcrowding and inefficient patient flow are significant operational challenges, often amplified by the volume of patients sustaining fall‐related injuries. This study predicts hospital admission for these encounters by leveraging unstructured clinical text: triage chief complaints (reasons for seeking care) and final emergency physician (EP) diagnoses. The primary objective is to develop a clinical decision support system that provides early, evidence‐based disposition predictions, serving as an operational alert mechanism to assist in the admission decision‐making process. We developed a “premodel” using triage data and a “postmodel” incorporating EP diagnoses. Analyzing 14,433 encounters, results across 12 algorithms demonstrated significant improvement when EP diagnoses were included. For top‐performing ensemble models, the receiver operating characteristic area under the curve—a measure of diagnostic accuracy—increased from 0.83 to 0.92, quantifying the value of integrating advanced natural language processing into this AI‐enhanced system. To foster clinician trust, we employed model interpretability techniques, specifically local interpretable model‐agnostic explanations and SHapley Additive exPlanations. These methods explain individual predictions by identifying influential clinical terms such as “fracture” and “head injury.” These high‐accuracy predictions provide critical inputs for future bed allocation models. This research underscores the potential of integrating text mining and machine learning to provide actionable insights that assist physician decision‐making and streamline ED outflow. Future research should focus on multi‐center external validation to assess generalizability across diverse healthcare settings.

The complexities, boundaries, and definitions of morality policy have long intrigued policy scholars. Within the study of policy diffusion, scholars have considered whether and how morality policies spread differently than those with less morality content. Of course, morality content is a dimension, not a simple dichotomy. Victor David Cruz-Aceves’s book, The Diffusion of Morality Policies, is an important step forward for this strain of research. While this book still has the rawness of a di...

The widespread outbreak of a new Coronavirus (COVID-19) strain has reminded the world of the destructive effects of pandemic and epidemic diseases. Pandemic outbreaks such as COVID-19 are considered a type of risk to supply chains (SCs) affecting SC performance. Healthcare SC performance can be assessed using advanced Management Science (MS) and Operations Research (OR) approaches to improve the efficiency of existing healthcare systems when confronted by pandemic outbreaks such as COVID-19 and Influenza. This paper intends to develop a novel network range directional measure (RDM) approach for evaluating the sustainability and resilience of healthcare SCs in response to the COVID-19 pandemic outbreak. First, we propose a non-radial network RDM method in the presence of negative data. Then, the model is extended to deal with the different types of data such as ratio, integer, undesirable, and zero in efficiency measurement of sustainable and resilient healthcare SCs. To mitigate conditions of uncertainty in performance evaluation results, we use chance-constrained programming (CCP) for the developed model. The proposed approach suggests how to improve the efficiency of healthcare SCs. We present a case study, along with managerial implications, demonstrating the applicability and usefulness of the proposed model. The results show how well our proposed model can assess the sustainability and resilience of healthcare supply chains in the presence of dissimilar types of data and how, under different conditions, the efficiency of decision-making units (DMUs) changes.

Abstract Religion is an indelible force in society, yet research examining its influence on consumption, particularly in the context of financial well‐being is lacking. Thus, this paper presents a conceptual framework of factors influencing, and outcomes associated with, the effects of religion on financial well‐being. Specifically, this paper introduces a conceptual framework aimed at understanding how religion influences financial decisions and well‐being, both from a consumer and a business perspective. Focus groups were conducted with consumers and financial practitioners to support the development of the conceptual framework. Most novel to this framework is the identification of potential process mechanisms explaining this relationship, including trust, affect, risk propensity, and perceived personal control. The framework concludes with potential interventions targeted at consumers and businesses to improve financial well‐being. This propositions‐based conceptual framework serves as a research agenda to guide and aid scholars, consumer advocacy groups, policymakers, and marketers in promoting greater financial well‐being.

In the current world of organizations, it is no surprise that discrimination exists against women. Many organizations, however, exhibit structural discrimination and little is done to combat this. This is no more evident than within law enforcement organizations. This research focuses on the disparate treatment, as defined by the Ninth Circuit Court of Appeals, as the barrier keeping women becoming Sheriff. Current data show women make up less than one-half of one percent of all sheriffs nationwide. This research suggests women hold leadership competencies that the field of law enforcement is seeking. By more women becoming the top executive of these agencies, the discriminatory landscape within law enforcement agencies will begin to change.

Collection of DNA for genetic profiling is a powerful means for the identification of individuals responsible for crimes and terrorist acts. Biologic hazards, such as bacteria, endospores, toxins, and viruses, could contaminate sites of terrorist activities and thus could be present in samples collected for profiling. The fate of these hazards during DNA isolation has not been thoroughly examined. Our goals were to determine whether the DNA extraction process used by the Royal Canadian Mounted Police eliminates or neutralizes these agents and if not, to establish methods that render samples safe without compromising the human DNA. Our results show that bacteria, viruses, and toxins were reduced to undetectable levels during DNA extraction, but endospores remained viable. Filtration of samples after DNA isolation eliminated viable spores from the samples but left DNA intact. We also demonstrated that contamination of samples with some bacteria, endospores, and toxins for longer than 1 h compromised the ability to complete genetic profiling.

The policing profession is fraught with countless hazards, not the least of which is being killed in the line of duty. Although yearly data are collected by the United States Department of Justice (DOJ) pertaining to police officers killed and/or injured in the line of duty, the department has limited itself primarily to age, race/ethnicity, gender, length of service, modality of injury/death and location. In addition to these variables, the authors collected data that included whether the slain officers were married and had children. Analysis of the data suggested that officers with greater social investments (wife and children) were less likely to be killed, and that younger officers had a lower probability of being killed in the line of duty than did officers with more experience.

Abstract An extensive literature review was conducted of four problem‐based approaches to professional development: (1) case study, (2) goal‐based scenario, (3) problem‐based learning, and (4) action learning. The review comparatively analyzed the training designs of these four approaches and found key differences in the nature of their case problems and training strategies. Specifically, the analysis found that case problems are ill structured in action learning and problem‐based learning, are moderately structured in a goal‐based scenario, and are fairly well structured in the case study approach. In addition, it was found that prototypical problems are used to a much greater extent in the problem‐based learning and goal‐based scenario approaches than they are in the other two approaches. Furthermore, the analysis found that the case study approach uses the most expert‐oriented training strategy, the goal‐based scenario approach uses a more learner‐oriented strategy than the case study approach, and the problem‐based learning and action learning approaches use strongly learner‐oriented strategies. These design differences suggest that the case study and goal‐based scenario approaches are more likely to result in single‐loop learning and to foster the ability to solve well‐structured problems, whereas the problem‐based learning and action learning approaches are more likely to lead to double‐loop learning and to promote the ability to solve ill‐structured problems. Implications of these findings for the design and research of problem‐based approaches to professional development are discussed.