This review focuses on vaccine distribution and allocation in the context of the current COVID-19 pandemic. The implications discussed are in the areas of equity in vaccine distribution and allocation (at a national level as well as worldwide), vaccine hesitancy, game-theoretic modeling to guide decision-making and policy-making at a governmental level, distribution and allocation barriers (in particular in low-income countries), and operations research (OR) mathematical models to plan and execute vaccine distribution and allocation. To conduct this review, we adopt a novel methodology that consists of three phases. The first phase deploys a bibliometric analysis; the second phase concentrates on a network analysis; and the last phase proposes a refined literature review based on the results obtained by the previous two phases. The quantitative techniques utilized to conduct the first two phases allow describing the evolution of the research in this area and its potential ramifications in future. In conclusion, we underscore the significance of operations research (OR)/management science (MS) research in addressing numerous challenges and trade-offs connected to the current pandemic and its strategic impact in future research.
PurposeThe purpose of this study is to introduce new tools to develop a more precise and focused bibliometric analysis on the field of digitalization in healthcare management. Furthermore, this study aims to provide an overview of the existing resources in healthcare management and education and other developing interdisciplinary fields.Design/methodology/approachThis work uses bibliometric analysis to conduct a comprehensive review to map the use of the unified theory of acceptance and use of technology (UTAUT) and the unified theory of acceptance and use of technology 2 (UTAUT2) research models in healthcare academic studies. Bibliometric studies are considered an important tool to evaluate research studies and to gain a comprehensive view of the state of the art.FindingsAlthough UTAUT dates to 2003, our bibliometric analysis reveals that only since 2016 has the model, together with UTAUT2 (2012), had relevant application in the literature. Nonetheless, studies have shown that UTAUT and UTAUT2 are particularly suitable for understanding the reasons that underlie the adoption and non-adoption choices of eHealth services. Further, this study highlights the lack of a multidisciplinary approach in the implementation of eHealth services. Equally significant is the fact that many studies have focused on the acceptance and the adoption of eHealth services by end users, whereas very few have focused on the level of acceptance of healthcare professionals.Originality/valueTo the best of the authors’ knowledge, this is the first study to conduct a bibliometric analysis of technology acceptance and adoption by using advanced tools that were conceived specifically for this purpose. In addition, the examination was not limited to a certain era and aimed to give a worldwide overview of eHealth service acceptance and adoption.
T his chapter provides an overview of cognitive case formulation, its purpose, and key areas of patient assessment that contribute to it. We then review the extant empirical evidence for cognitive case formulation and share our thoughts about its potential role in the newer cognitive behavioral therapy (CBT) treatments. A complete guide to developing and using cognitive case formulation is beyond the scope of this chapter; there are several excellent resources available that address this topic more fully (e.g., Eells, 2007;Persons, 2008). Here, we focus on the definition of case formulation and then move on to the broader topics of function and utility.
Background: Serious mental illness is a major risk factor for aggression and violence. The aim of the present study was to develop and test an algorithm to predict inpatient aggressions that involve a risk of harm to self or others.Methods: This work is based on a retrospective study aimed to investigate the prediction of risk of harm and aggressions at St. Joseph's Healthcare Hamilton, between 2016 and 2017. An analysis of the risk factors most strongly associated with harmful incidents is, followed by the description of the process involved in the development of a predictive model which estimates the risk of harm.Results: The e ciency of the model developed is nally evaluated, showing an overall accuracy of 75%: the speci city to identify episodes considered not at risk of harm is equal to 91.85%, whereas the sensitivity to identify episodes considered harmful is equal to 28.57%. a Conclusions: The model proposed can be seen as a seminal project towards the development of a more comprehensive, precise and effective tool capable to predict the risk of harm in the inpatient setting.
Background: Serious mental illness is a major risk factor for aggression and violence. The aim of the present study was to develop and test an algorithm to predict inpatient aggressions that involve a risk of harm to self or others.Methods: This work is based on a retrospective study aimed to investigate the prediction of risk of harm and aggressions at St. Joseph’s Healthcare Hamilton, between 2016 and 2017. An analysis of the risk factors most strongly associated with harmful incidents is, followed by the description of the process involved in the development of a predictive model which estimates the risk of harm. Results: The efficiency of the model developed is finally evaluated, showing an overall accuracy of 75%: the specificity to identify episodes considered not at risk of harm is equal to 91.85%, whereas the sensitivity to identify episodes considered harmful is equal to 28.57%. aConclusions: The model proposed can be seen as a seminal project towards the development of a more comprehensive, precise and effective tool capable to predict the risk of harm in the inpatient setting.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.