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No studies have examined preoperative handoffs from the intensive care unit (ICU) to OR. Given the risk of patient harm, the authors developed a standardized ICU to OR handoff using a previously published handoff model. The purpose of this paper is to determine whether a standardized ICU to OR handoff process would increase the number of team handoffs and improve patient transport readiness.

The intervention consisted of designing a multidisciplinary, face-to-face handoff between sending ICU providers and receiving anesthesiologist and OR nurse, verbally presented in the I-PASS format. Anticipatory calls from the OR nurse to the ICU nurse were made to prepare the patient for transport. Data collected included frequency of handoff, patient transport readiness, turnover time between OR cases, and anesthesia provider satisfaction.

In total, 57 audits were completed. The frequency of handoffs increased from 25 to 86 percent (p<0.0001) and the frequency of patient readiness increased from 61 to 97 percent (p=0.001). There were no changes in timeliness of first start cases and no significant change in turnover times between cases. Anesthesia provider satisfaction scores increased significantly.

A standardized, team based ICU to OR handoff increased the frequency of face-to-face handoffs, patient readiness and anesthesia provider satisfaction within increasing turnover between cases.

Although studies have identified the transition of patients from the ICU to the OR as a period of increased harm, the development of a preoperative ICU to OR handoff had not been described. This intervention may be used in other institutions to design ICU to OR transitions of care.

Did the expansion of democratic institutions play a role in determining central government spending behavior in the 19th and 20th centuries? The link between democracy and increased central government spending is well established for the post-Second World War period, but has never been explored during the first “wave of democracy” and its subsequent reversal, that is 1870–1938. The main contribution of this paper is the compilation of a dataset covering 24 countries over this period to begin to address this question. Utilizing various descriptive techniques, including panel data regressions, we explore correlations between central government spending and the institutional characteristics of regimes. We find that the data are consistent with the hypothesis that democracies have a broader need for legitimization than autocracies as various measures of democracy are associated with higher central government spending. Our results indicate that the extension of franchise had a slight positive impact on central government spending levels, as did a few of the other democracy variables. We also find that early liberal democracies spent less and monarchies more than other regimes; debt increases spending; and participation in the Gold Standard reduced government spending substantially.

This case details the history of Southwest Airlines from its inception in 1971 until 2004. The case provides details of Southwest's business model and reasons for its success. It ends with a description of the company's competitive pressures in 2004. The case can be used for a course in service operations or strategy.

The experience of going natural or deciding to rock an afro, wash-n-go, twist-out, or braided updo on a campus where similar faces are rarely seen in the classroom, in the residence hall, or even in the nearby local community can be one fraught with numerous personal and political identity tensions. Nonetheless, this is the experience for many Black women collegians, both undergraduate and graduate, who choose to wear their natural hair in its kinky, curly, coily, or afro-textured state while in college. Through an intersectional perspective we examine the stories of six Black women and their experiences with their hair, identities, and community as they transitioned to wearing their natural hair. Through this study we center the bodies, voices, and needs of Black women as they navigate the complexities of thriving in a Eurocentric environment (i.e., a predominantly white university). This chapter ends with a call for greater attention to the meaningfulness of Black women's hair and a discussion of strategies for campus agents to more intentionally support Black women throughout their development in college.

Sociology promotes and describes public health, helping to explain macro-social dynamics of mental health care through studies of organizations, networks, and systems of care.

This chapter summarizes sociological research on mental health care organizations and systems, illustrating a macro-social perspective by examining the problem of transitions in care for young adults. Summary findings from a regional mental health services research project describe a system of care that includes 100 organizations. This system helps young adults with mental health needs.

The scope and management of care involves a focus on modes of treatment supported by research evidence and delivered effectively by people with cultural competencies. Care and continuity of care are delivered through coordinated systems of inter-organizational networks, linking organizations and providers. Active inter-organizational linkages are needed to support mental health for young adults during challenging and sometimes difficult transitions.

This research summarizes original and regional data on mental health care organizations within a regional system of care. Practical implications include support for the importance of coordination, transition planning, and cultural competence within and among organizations. Sociological and original research on organizations and systems should continue to elaborate the needs and values of mental health services for regional planning and public health.

The dislocation between what people are and what they have to do produces an erosion in values, dignity, spirit, and will (Maslow, Stephens, and Heil (1998)). For educators in the new millennia, these are trying times. The lack of prestige and autonomy combined with exponentially increasing demands contributes to the high rate of attrition, stress, and burnout. To stay the course and remain vibrantly engaged in the profession will require an extraordinary degree of self-care. This chapter offers a lens for viewing systemic factors affecting the educational landscape and underscores the need for educators to employ individual and collective strategies to preserve health and well-being.

This issue is a selected bibliography covering the subject of leadership.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.